{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of Analyzers using Input Perturbations\n",
    "\n",
    "This notebook will guide you through an example of how to evaluate analyzers via perturbing the input according to the importance that the analysis method attribute to input regions.\n",
    "\n",
    "The input images are divided into quadratic regions that are sorted according to their importance w.r.t. to the pixel-wise saliency scores assigned by those analyzers. Then, the information content of the image is gradually destroyed by perturbation of the most important regions. The effect of this perturbation on the classifier performance is measured. This procedure is repeated several times.\n",
    "\n",
    "We expect that the classifier performance drops quickly if important information is removed and remains largely unaffected when perturbing unimportant regions.\n",
    "\n",
    "Thus, different analyzers can be compared by measuring how quickly their performance drops, i.e. the quicker the classifier performance drops after input perturbation w.r.t. to the prediction analysis, the better the analyzer is capable of identifying the input components responsible for the output of the model.\n",
    "\n",
    "Similarly, several models can be compared, e.g. with random perturbations on the data, towards their resilience to noisy input data: The faster the model prediction declines with ongoing perturbations, the more susceptible the classifier is to noise.\n",
    "\n",
    "Reference:\n",
    "\n",
    "*[Samek et al.](http://dx.doi.org/10.1109/TNNLS.2016.2599820)*, \"Evaluating the visualization of what a deep neural network has learned.\" *IEEE transactions on neural networks and learning systems* 28.11 (2017): 2660-2673.\n",
    "\n",
    "-----\n",
    "\n",
    "Parts of the code that do not contribute to the main focus are outsourced into utility modules. To learn more about the basic usage of **iNNvestigate** have look into this notebook: [Introduction to iNNvestigate](introduction.ipynb) and [Comparing methods on MNIST](mnist_method_comparison.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "\n",
    "import imp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import keras\n",
    "import keras.backend\n",
    "import keras.models\n",
    "\n",
    "import innvestigate\n",
    "import innvestigate.utils as iutils\n",
    "\n",
    "from innvestigate.tools import Perturbation, PerturbationAnalysis\n",
    "\n",
    "# Use utility libraries to focus on relevant iNNvestigate routines.\n",
    "eutils = imp.load_source(\"utils\", \"../utils.py\")\n",
    "mnistutils = imp.load_source(\"utils_mnist\", \"../utils_mnist.py\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data\n",
    "Then, the MNIST data is loaded in its entirety, formatted according to the specifications of the Keras backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "# returns x_train, y_train, x_test, y_test as numpy.ndarray\n",
    "data_not_preprocessed = mnistutils.fetch_data()\n",
    "\n",
    "# Create preprocessing functions\n",
    "input_range = [-1, 1]\n",
    "preprocess, revert_preprocessing = mnistutils.create_preprocessing_f(data_not_preprocessed[0], input_range)\n",
    "\n",
    "# Preprocess data\n",
    "data = (\n",
    "    preprocess(data_not_preprocessed[0]), data_not_preprocessed[1],\n",
    "    preprocess(data_not_preprocessed[2]), data_not_preprocessed[3]\n",
    ")\n",
    "\n",
    "num_classes = len(np.unique(data[1]))\n",
    "label_to_class_name = [str(i) for i in range(num_classes)]\n",
    "\n",
    "x_test, y_test = data[2:]\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "test_sample = np.copy(x_test[0:1])\n",
    "generator = iutils.BatchSequence([x_test, y_test], batch_size=256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next part trains and evaluates a CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 7s 114us/step - loss: 0.1427 - acc: 0.9560\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0370 - acc: 0.9886\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0214 - acc: 0.9932\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 4s 70us/step - loss: 0.0137 - acc: 0.9953\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 4s 70us/step - loss: 0.0107 - acc: 0.9963\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 4s 70us/step - loss: 0.0087 - acc: 0.9970\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0056 - acc: 0.9978\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0065 - acc: 0.9979\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0050 - acc: 0.9982\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0049 - acc: 0.9985\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0037 - acc: 0.9990\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0024 - acc: 0.9993\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0039 - acc: 0.9987\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0036 - acc: 0.9989\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0028 - acc: 0.9991\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0027 - acc: 0.9991\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 4s 70us/step - loss: 6.7418e-04 - acc: 0.9997\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 4s 69us/step - loss: 0.0036 - acc: 0.9992\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0042 - acc: 0.9987\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 4s 68us/step - loss: 0.0022 - acc: 0.9993\n",
      "Scores on test set: loss=0.056781358060250225 accuracy=0.9901\n"
     ]
    }
   ],
   "source": [
    "# Create & train model\n",
    "if keras.backend.image_data_format == \"channels_first\":\n",
    "    input_shape = (1, 28, 28)\n",
    "else:\n",
    "    input_shape = (28, 28, 1)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Conv2D(32, (3, 3), activation=\"relu\", input_shape=input_shape),\n",
    "    keras.layers.Conv2D(64, (3, 3), activation=\"relu\"),\n",
    "    keras.layers.MaxPooling2D((2, 2)),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(512, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\"),\n",
    "])\n",
    "\n",
    "scores = mnistutils.train_model(model, data, batch_size=128, epochs=20)\n",
    "print(\"Scores on test set: loss=%s accuracy=%s\" % tuple(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perturbation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup analyzer and perturbation\n",
    "The perturbation analysis takes several parameters:\n",
    "* `perturbation_function`: This is the method with which the pixels in the most important regions are perturbated. You can pass your own function or pass a string to select one of the predefined functions, e.g. \"zeros\", \"mean\" or \"gaussian\".\n",
    "* `region_shape`: The shape of the regions that are considered for perturbation. In this case, we use single pixels. Regions are aggregated (\"pooled\") using a (customizable) aggregation function that is average pooling by default. The input image is padded such that it can be subdivided into an integer number of regions.\n",
    "* `steps`: Number of perturbation steps. \n",
    "* `regions_per_step`: In each perturbation step, the `regions_per_step` regions are perturbed.\n",
    "\n",
    "Feel free to play around with different analyzers, e.g. by selecting them from the `methods` list via `selected_methods_indices`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using method(s) \"['random', 'gradient', 'smoothgrad', 'input_t_gradient']\".\n"
     ]
    }
   ],
   "source": [
    "perturbation_function = \"gaussian\"\n",
    "region_shape  = (7, 7) \n",
    "steps = 15\n",
    "regions_per_step = 1  # Perturbate 1 region per step\n",
    "\n",
    "# Scale to [0, 1] range for plotting.\n",
    "def input_postprocessing(X):\n",
    "    return revert_preprocessing(X) / 255\n",
    "\n",
    "noise_scale = (input_range[1]-input_range[0]) * 0.1\n",
    "ri = input_range[0]  # reference input\n",
    "\n",
    "\n",
    "# Configure analysis methods and properties\n",
    "methods = [\n",
    "    # NAME                    OPT.PARAMS                POSTPROC FXN               TITLE\n",
    "\n",
    "    # Show input\n",
    "    (\"input\",                 {},                       input_postprocessing,      \"Input\"),\n",
    "\n",
    "    # Function\n",
    "    (\"gradient\",              {\"postprocess\": \"abs\"},   mnistutils.graymap,        \"Gradient\"),\n",
    "    (\"smoothgrad\",            {\"noise_scale\": noise_scale,\n",
    "                               \"postprocess\": \"square\"},mnistutils.graymap,        \"SmoothGrad\"),\n",
    "\n",
    "    # Signal\n",
    "    (\"deconvnet\",             {},                       mnistutils.bk_proj,        \"Deconvnet\"),\n",
    "    (\"guided_backprop\",       {},                       mnistutils.bk_proj,        \"Guided Backprop\",),\n",
    "    (\"pattern.net\",           {\"pattern_type\": \"relu\"}, mnistutils.bk_proj,        \"PatternNet\"),\n",
    "\n",
    "    # Interaction\n",
    "    (\"pattern.attribution\",   {\"pattern_type\": \"relu\"}, mnistutils.heatmap,        \"PatternAttribution\"),\n",
    "    (\"deep_taylor.bounded\",   {\"low\": input_range[0],\n",
    "                               \"high\": input_range[1]}, mnistutils.heatmap,        \"DeepTaylor\"),\n",
    "    (\"input_t_gradient\",      {},                       mnistutils.heatmap,        \"Input * Gradient\"),\n",
    "    (\"integrated_gradients\",  {\"reference_inputs\": ri}, mnistutils.heatmap,        \"Integrated Gradients\"),\n",
    "    (\"deep_lift.wrapper\",     {\"reference_inputs\": ri}, mnistutils.heatmap,        \"DeepLIFT Wrapper - Rescale\"),\n",
    "    (\"deep_lift.wrapper\",     {\"reference_inputs\": ri, \"nonlinear_mode\": \"reveal_cancel\"},\n",
    "                                                        mnistutils.heatmap,        \"DeepLIFT Wrapper - RevealCancel\"),\n",
    "    (\"lrp.z\",                 {},                       mnistutils.heatmap,        \"LRP-Z\"),\n",
    "    (\"lrp.epsilon\",           {\"epsilon\": 1},           mnistutils.heatmap,        \"LRP-Epsilon\"),\n",
    "]\n",
    "\n",
    "# Select methods of your choice\n",
    "selected_methods_indices = [0, 1, 2, 8]\n",
    "selected_methods = [methods[i] for i in selected_methods_indices]\n",
    "print('Using method(s) \"{}\".'.format([method[0] for method in selected_methods]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main loop below will now instantiate the analyzer objects based on the loaded/trained model and the analyzers' parameterizations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model without trailing softmax\n",
    "model_wo_softmax = iutils.keras.graph.model_wo_softmax(model)\n",
    "\n",
    "analyzers = [innvestigate.create_analyzer(method[0],\n",
    "                                        model_wo_softmax,\n",
    "                                        **method[1]) for method in selected_methods]\n",
    "for analyzer in analyzers:\n",
    "    analyzer.fit(data[0],\n",
    "                 batch_size=256, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Evaluate the model after several perturbation steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup perturbation\n",
    "The perturbation analysis consists of two parts:\n",
    "1. An object of the class `Perturbation` that performs the actual perturbation of input images. Here, we use (2, 2)-regions (i.e. single pixels) and add Gaussian noise to the original values of the most important pixels.\n",
    "2. An object of the class `PerturbationAnalysis` that computes the analysis, performes several perturbation steps and evaluates the model performance. In each step, the 1% most important pixels are perturbed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: random\n",
      "\n",
      "Method: gradient\n",
      "\n",
      "Method: smoothgrad\n",
      "\n",
      "Method: input_t_gradient\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scores_selected_methods = dict()\n",
    "perturbation_analyses = list()\n",
    "for method, analyzer in zip(selected_methods, analyzers):\n",
    "    print(\"Method: {}\".format(method[0]))\n",
    "    \n",
    "    # Set up the perturbation analysis\n",
    "    perturbation = Perturbation(perturbation_function, region_shape=region_shape, in_place=False)\n",
    "    \n",
    "    # Comment out to invert the perturbation order\n",
    "    # perturbation.aggregation_function = lambda x, axis: -np.mean(x, axis=axis)\n",
    "    \n",
    "    perturbation_analysis = PerturbationAnalysis(analyzer, model, generator, perturbation, recompute_analysis=False,\n",
    "                                                steps=steps, regions_per_step=regions_per_step, verbose=False)\n",
    "    \n",
    "    scores = perturbation_analysis.compute_perturbation_analysis()\n",
    "    \n",
    "    # Store the scores and perturbation analyses for later use\n",
    "    scores_selected_methods[method[0]] = np.array(scores)\n",
    "    perturbation_analyses.append(perturbation_analysis)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the perturbation curves and compute area over the perturbation curve (AOPC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f405c0616d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "aopc = list()  # Area over the perturbation curve\n",
    "baseline_accuracy = scores_selected_methods[\"random\"][:, 1]\n",
    "for method_name in scores_selected_methods.keys():\n",
    "    scores = scores_selected_methods[method_name]\n",
    "    accuracy = scores[:, 1]\n",
    "    aopc.append(accuracy[0] - np.mean(accuracy))\n",
    "    \n",
    "    label = \"{} (AOPC: {:.3f})\".format(method_name, aopc[-1])\n",
    "    plt.plot(accuracy - baseline_accuracy, label=label)\n",
    "    \n",
    "plt.xlabel(\"Perturbation steps\")\n",
    "plt.ylabel(\"Difference of accuracy to random analyzer\")\n",
    "plt.xticks(np.array(range(scores.shape[0])))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, a steeper decrease shows a better identification of the relevant information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Plot perturbed sample\n",
    "Finally, we plot the perturbations on a selected test sample and show them along with the respective analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ff1fc8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3cMmWuvAoYv19Yq3NMqFTtpYCHXEJAkstClmH4q1+FrfZC8+p84tcY0uhM6OofTDgUBs39+0O3it1hkNBEcfQ9rhEqrcHxrX5OCN3A+OOfy919QeFbqMyP5yJy2u1A7gAGIsLEYmqgeJQOAC/IYWc6+cRzPWgffATnPPiEaCdtfYCG1KcRgXFwc869aXMhaNxfbOptbaHtfalCFM4LIqLY7Beq40bV2cAna21Xa21r5QAjYOimPjlW+fIjcP1zX1Ad2vtX6JpeAor2+/ug0CmtfYLQNfe1jncfowogUKguNqodc7pyoQSWve21v7DFvaAB2ut//E/h/zBSSNvBKoDZwEv4xKPAiREu3xF4BEHxB3ks4twls1OwesHcWEF1XAy9fVAYvBZZVwi3Ka4ZHoL5HsHuK8pQX4GFzt9Hk6aHRf22X9xCWJrAn8HXgMuxm0gngH+U+Bel+MMLR2D1w/jJogDcgueSe9I198B3jsxqLsvcZL0b4CJQLNDPX+gK24hKtbsrtFun8XJEZcr6HScwulBnJS9RVnhF/bdY4N6jKX6+zbg17QgP5zxOw8nkx2GU/N9B5wZbW4Rqr9OuHG2ebS5/d46FJ5Bn5sV9LkGwRj7ITAk2tyKuw5xns7zDnbfUszvUH2wEs5oMSh4XR4XZjgy2twiUYfBZwNwxuDUaHMrjjos8D2Z66dykLmeQ6wNY5Bfj4PwK1dG6nA2TvGdGE2OEeT3NQfYT5SxNtqlsP8zRjn+7jossYfgf0rnD25RGVfgvVTCFsu4nCxfBu8n4zbqz0W77L+Dc13c8daXAS2D947FJaa6K3hdHpeLZSIu2/3nhAwVDYLvb8QpRNoXuH9UF6m4vA6zgT8Fr3sD34V93gEnrfsoeP0XnMywTdg1XXBGuAGloL4OZlBLw3k0s3BKHnCJxV4F7j3E/Y7Heeyzg/qN+sa+ODniJPi7cNL1bTgDVMcyxK8X7ljCd3Ge+1uB8rHMj9DGvg7BIiB4XRPnPWsQy/wKfKctLuxnKs7zeVW0x8zi4ij3wIXavRqMw7twDo60WOcXfi+cI2AGcEu0664Y669c8PtOXJLRL4FfcYv0OmWBY9h3auKMwZ/ics5cVJb4BdeEz/WTOLBhpiQdaCXKjxLe0EeJY3xJcowCv3JlmV80fmKhDqP6gPxP9H44gFEmeD8Jp7KYHfZeRVwM9pDg9TDcZj6iSonfye83VnTcUYkP4DYE7+HyAKwHqgafX40LyRELaR2cRH05znN2XNi9jwEqRrn+CvLrjltovYxbVE4L3m+JOz5yGC6EayPwH6Bm8HmbYPD5V4H7xxe4f0l6JQ6kBjkVpwi5FKgVvHcBsBeoFnbdX4AJQKsD3KMCMAp3zHmlKLfRSHFsi0uudiWQUgb5NcVt7G8qK/wK3utA945lfgXu0SQYp26OZv1FkqPcG2cIj9o4E2F+FYC+QFJZ4UfISCqn95wTzfqLZB3i1kM34PKvlIlxtMA9ylMK5vqyzu9o4Oj5xTa/WOQYtQflf0rHD26BdU3wUzt4ryfO69447LoncPlJyuHCfF4EHsctYG6PUtkPZFwqqAIqF/Z3ZeB+ID3sve+A24K/u+GsniPCPq+GSwCcDzQM3guXQEfMqn0QfgUNMhVxRqRknEHmn0GdvI5Lctw+4PAVzjt2ath3U3GbhzicEug6fmuQKUmLfSLwZ4KQubD3E3DJGBcDtwX1MQWXrDEF+Am4NOz63jgpYq1C/M9yBZ+p5xiT/ErEmFHM/OYVhl9wfSzyK3X1F606pAS9n9Gow5L8Kev1F606LOvjKLE7D5Y6fkcDR88vtvnFOscSeUD+J7o/HEDaiFN+9MflNvgQpySZDZwQfP4+8EDY9a1wcdgS2tMHd5xtJu7UpiSi6O09AL8WuJOVvsTFD4o6pl7wexDO+54fdND6OEPVWFxulgoFuE8niEGPArduQRnCc8jUwYUG/Aqcj0vw9zUhNUwtXF6ZZ4PXo3Ex6DWC18NxoVoXBa8TS4rPAfg1wXkqq+EUPa8EfF/GeZ8b4eTzUnfxuLCsJ4PXd+Iy2ZcPXrcPXlc/TJ8oyQ1hiXFkf+9vSS22o1GHJbmZKHF+JflT1uvP16HnV9r5RYsjJTgXen6xze9o4Oj5xTa/ssKx1J5w4fH7YYwpF3YkWMETBsbiQnY+staejJPvfow7mQdcYz4r7PoaOPnWBcaYZGvtZ7gwp3rW2r9ba3Ns0EJLAsGJS38yxkyC0DGRxpiOxpiRuES9kshpBM6QAbDOGDMKd6Tr57hOmQb0t9Zm404uqouLIRRkAhZ3olOJIDhK73pjzAZcPT0DfGWMOd4Ycy4uBv4EnLHsVZxyZh+wM7jFZpxxqr8xJsVa+wDO8vtfY8wPuDwPT1hrXwSw1u4NnmmJHBNqjEkxxtxujFmBGzzbWGuzgo/PBv4HbAnaVD9c7pR1xphbgB9w1u2PgutfB9KBqcaYu3CD7jiccuiAsEU4lvpIES2Owstauy+SHEtBHeaVZX6RRrT5Rbr+IPocIw3PL7b5QfQ5Rnou9Pximx+UfY6eX2zzgzLIsbisPP6ndPzgkoE2LfBefVwI0o0EyWtxypcVwPVh1yXhYu3a4KyLK4IGeQ7uyMGHcXlZEgrcv0Q9oGH/dwlO+XI7oRMJhuFOkJgadt0FuLwxcprR1wSnn+CsqRtwp2uk4EKfahX4PxfgwrxKLCEgcCFuoOgXvG6JCyv7CBe6NBmYGXZ9JdyxiT0JJaX8W/B8rg5eG5whqlGU22hN3GD3LtADaI47FrEczmg2h7BQOVyOkXxckq73gJPDPqsRfO+Z4J6ncpCTszxHz8/zOzr4HQ0cPb/Y5nc0cPT8Ypvf0cDR84ttfmWVY1QfqP+JQIW6UyEew8XaNQeuxUm53sCpYTaEXfsuzrBRJey9zwgMNrgEs88Ay3DJRaOepDIolxgfngcWAs8RGGNwBpaXg/fig/ca4nLLDA9ef4QzbtyIS0x5GcFxoGH/Q0JDagXPdFj4+xHmlxoMJjcFrxOC3+m4kKspuGSaUwg7AQSnsHkfOC14/W9cEqvJB/gfv0koXIL1dxUw5RCfX4BTNaWGvTcLuC/sdRWc+kuSVJ8GrBZOlGBIz9HI0fPz/Eozv6OBo+cX2/yOBo6eX2zzOxo4en6xza+scvRhTWUPdwCdcEqLITjjywxr7dnW2guAXcaYG4Jr38flM+kHThaGsxh+BWCt/QL4m7W2ubX2EWutNcbElVToy8Fgrc03xlTGdZYpOEXPCcaYf+KOMvsEp4DpGHxlAy7E55Lg9UicxbQ7cIe19mlr7YQC/8MGvzdaa/9srX0p/P0Ioy7uuO7xwf/MDX6vw1lyE3AhZluAgWHfG4PLG3SrMWZrcI+/WWvPLPgPrENeJEkcArlAE2NMvDGmtjGmijGmnzGmRvD5EiAHl0dHcBPQ2hjzljHmddzpU8fgjFgEv3/GGdwgoBhpIodAWefo+Xl+pZkflH2Onl9s84Oyz9Hzi21+UPY5en6xzQ/KIseSsgL5n5L7wVkEL8bJu5YCfwz77C/A6uDvGrgQnzW4IxUX4FQlVQ9wz/iSKHsReX5OcPY8cC4uZ87tOOPGm8CosGs74ww37Q7EhxI8iaEQvKriwst6SNkIKXm64AaNPwH3ESSwKvD9loQpakohv9rAuqDtfQLswR1pPosgQzrOgv1eePlxoVvn4lRcNQvcs1zQhn+jEvIcPT/P7+jidzRw9Pxim9/RwNHzi21+RwNHzy+2+ZVVjlF/qP4nApXqTmF6B7dJfxB4rcDnOwiFvtyFOz76JqJ0GtER8BNDxZPA88HffXCGqHycdfSh4POM4POKwPHB3xIWFUeUQnsOw68STtU0PngdH1bm8rikv62AobjTtvqEP5ew+5To8Z9F5NgYOAm4HGetrgs8gAtTq4A74nsxLvQuH7jwAPfYjx9BZvXS8lPWOXp+nl9p5nc0cPT8Ypvf0cDR84ttfkcDR88vtvmVRY5Rf6D+JwKV6hrYT8CJuCzVs4BWYZ+/CswP/u6KC5UZEP79aHMoJMcncIl+v8PJz24G/ovLm7Mcl7G7Y7TLeoT8LsFJ9VoUeP9vOOtwJdxx4ecD1aJd3mLifBHuhK3mwevuwD+AlgWuM5SS/Eeeo+fn+cXWT1nn6PnFNr+jgaPnF9v8jgaOnl9s84t1jqJA8ChjMMbciAtbeh6XLGmjtfb64LN6uFOAXgheT8AlPrrPWrsxOiUuOowxj+A630W4k4uyjDFJwACgkg2OiY5VGGOm4k7aeh94EWeIOQ+4y1r7XDTLVlww7lj2XcaYirjk0xuttVcd4Lo4+9vj4GMCZZ2j56fXeX6lFGWdo+en18UkPyj7HD0/vS4m+UHZ5+j56XUxyQ/KDkefELjs4klcot8k4Huge2C4wFqbKYaZAB/jVBgVSryUvw+rgeXW2imBYcZYa3OstW+JYcYYE8tt/GJcPTbHqZ3a406c2s8wY0x0EzQfKYwxHYE7jDFTgI248K2HDnBdqR5ED4WyztHz0+s8v1KKss7R89PrYpIflH2Onp9eF5P8oOxz9Pz0upjkB2WLY3y0C+ARGVhrtxhjfgUycEdoP2qtzQm/JqyBPgs8bWNPRlUTmCWW0vDyB4YaW9o74KFgrf0FeMwYM869tHvkM+EXXBdr9SZYgRtAVwEXWWu3HeiiWK5Dyj5Hzw/Pr5SjrHP0/IhpflD2OXp+xDQ/KPscPT9imh+UIY4+rKkMwxhT2Vq7Pex1qbcWFgVilIl2OUoKxphyADZ6R2BHFAG/mDaoHQ5lnaPnF9so6/yg7HP0/GIfZZ2j5xf7KOscPb/YRyxz9MYZj5hHWTM6HW0IQs9sDCuADouyztHzi22UdX5Q9jl6frGPss7R84t9lHWOnl/soyxw9MYZDw8PDw8PDw8PDw8PDw8PjygilpOlenh4eHh4eHh4eHh4eHh4eMQ8vHHGw8PDw8PDw8PDw8PDw8PDI4rwxhkPDw8PDw8PDw8PDw8PDw+PKMIbZzw8PDw8PDw8PDw8PDw8PDyiCG+c8fDw8PDw8PDw8PDw8PDw8IgivHHGw8PDw8PDw8PDw8PDw8PDI4qIj3YBjjYYY2L57PJfrLU1DnVBXFycjY+Pp0aNGuTm5gKwa9cuAIwx+l79+vUBWLVqFdWrVwdgy5YtACQlJel1eXl5ANSpU4esrCwAypUrJ/9LX+/btw+A/Px8fS8pKQmAX3/9Va/Pzs4GID4+Xu9fq1YtANatW3dYfolxFWyF+JRDXbIf8iolFfra/HKFvtRdX/hbA5CzNvOw/KDst1HPr1TDt1E8v1IO30YpXfxatGgBwM8//wxAq1at+OmnnwCoWrUqANu3b9frt2zZElP86tSpA0D58uUB2LlzJ9WqVQNCvHJzc3X9k5WVFfU2mp6eDoTWcKmpqYArb0JCAgB79+4FwFpLYmIigK7lEhMTMcYA6LrNWou1rsgbN24s9jo89thj2bBhAwAVKlQAICEhgZ07dwKhtWdubq6uL5OTk/fjmZeXR05Ozn732L17Nykpbt0o6+H8/Hy9rmLFivq/5L4rVqwodn5VqlTR+8v/lP8rvKS88rfwk9fx8fHazqSudu/erfeVe+3Zs0fX9rJ2z83NpWbNmgD8+OOPEWmjSUlJ1KjhbivltNayY8eO/fgkJSXp8xdIe0xOTtZ6knpNSEjQ+27btk3fk/tWqlQJgPXr19OkSRMAli1bdliO5cuXtykpKVSpUoXMzEwg1G4qVqyo/0v2Kdu3b9fPpV/k5ubq2CD9KCcnh8qVKwOufgCtj3Xr1ulnUv69e/dqm5DfeXl5WsdpaWkA/PTTT/oMC9MHk5OTbWpqKgkJCVpe6U+VKlXS8gp27Nihz1L6W35+vtaD1EtCQoLu46RtShkhtJ+UfWLlypWVq3wvPj6eX375Zb/rKlSooG15zZo1hWqjkYA3zngUBasOd0F8fDw1a9bk8ssvZ926dQB8++23gOtoGzduBOC+++4D4PLLL+ePf/wjAM8//zwATZs21QlSBqZrr72WiRMnAqHFlnSgqlWr6uAvg1BycjLNmzcHYMqUKYAbcBYtWgSgg2xWVhZ/+9vfAPjnP/95WH4V4lPoXv3cw100QGPJAAAgAElEQVSm2NarUaGv3VOtaEK2HccU6XJ+/MfVh+VXBlDWOXp+sY+yztHzi33EFMdnn30WgD/96U8AzJ07l6FDhwIwaNAgAD788EO9/oUXXogpfsIrIyMDgFmzZnHuuW4d8r///Q9wm0JZ/0yYMCHq/GRdJWs4qY+PP/5YN+hr1qwB3MawQYMGQGhT1bBhQ90wff/994DbLO7ZsweABx54oNg5vvLKK4wdOxaAtm3bAlC7dm2++OILwBk3ANauXavry86dOwMhJ+D27dtZvnw5AMcddxzg1sD9+vUDYM6cOXrd6tWrAWjXrp3+L9nYDxgwoNj59enTh0aNGu33P40x1K1bF3BtCFz/kbV6x44dAfR1Wlqa1oG8991339GsWTMgZERYvnw5f/jDHwCYPHky4J6btIuzzz47Im20UaNGXH755UBoI5+Tk8Pnn38OQIcOHQBn0P3xxx+BkAFm7dq1gKvTr776CggZL2rXrs1ll10GwLvvvqtcP/30UwB69uwJwNixY3n88ccB6Nev32E5pqSkMGjQIAYOHMj1118POOOylPXNN98EYPTo0QB89NFHtG7dGoD58+cDrh5kbJD6Xb58Of379wdc/QBcfPHFANxyyy2cfPLJAFr+zMxMbfPyjH799Vc1XgwbNgyA8847j27dugFw7733HpZfamoqf//730lPT2fhwoUAfPnllwB07dqVhg0bAiHjyKeffkqPHj0A1IC0a9curYevv/4agJo1a+o4Iu1W2ltcXBzjxo0DQoamk046ic8++0y/C27vKNeJwal169baB6+44oqojaPeOONR7MjPz2fixInamc466yzALSLE6CKLiM2bN+sAeuyxxwKuc2zduhWA2bNnAzBx4kS1uoqnSDrdsGHDePHFF4GQt2bBggU6kH788ccAzJs3j06dOgFwxhlnAG4gEGOPh4eHh4eHR2xBNrxiCLjiiit0gS+bi06dOunmK9Yg3mUxEhx33HG6SZHNWGpqqm6aJkyYUPKFLABxmMlG56233gJg69atunaTzX39+vVZsWIFENqQzZw5k4suugiAe++9F4BRo0bpRjoSGDZsGDfddBMAL730kpZ/1qxZAPTu3RuAvn37ap2Iw0+MEosXL9Z1qLTLli1bqkOyb9++gNtQnnjiiUDIKFChQgVtw5FAkyZNdOMtSqYvv/xSyy5t6rvvvtNnL/V2/vnnA87IKYoUUUC0adOGli1bAjB9+nQAzjzzTL1OjATGGCZNmhQxflIWUVKIMmb79u2cd955AGpYWrp0qZZPyt61a1fA9TMxGsj+YNGiRXzzzTcA+ym/xJAi7fLCCy/Uei8M0tPT+de//sWUKVO0bUifbtmypSpiXn75ZcCNcWJEkc/S0tK0Dck+qk6dOjzwwAMAuu95+umn9RlJ3Z1++ukAzJgxQ+taDI6tW7dWY+n9998PwJAhQ4rURnNzc1m/fj0ZGRk6FohyZeXKlboXFIPXAw88wCuvvAKgBr/ExERV8Ehd1apVSzmLoemdd94B4JhjjtFnKG1v7NixaoR87bXXAGdQO/XUUwF0z3ncccepsSea8MYZj2JFUlISTZo0oWrVqpxwwgkAaplMTU1VxYoMGnfddZdOEnLdqFGjuPXWW4GQwmXZsmUqcZXvzpgxA4CrrrqKLl26ANC9e3fAdXqxFouHY/DgwTz66KMA3HzzzYCTO8oA7eHh4eHh4RFbmDt3LoCuG7Kzs9U4IJvo4447TkNiYg1iHDj++OMBp3qQtYxsPt5++21VKZcGiFFs3rx5QEg5s3z5ct38LFmyBHDqjJUrVwIhY0daWpqu62QDtW3btt+EohQnhg4dyvvvvw+EVAaTJ09WVY9s+LKysvjhhx8Ap0YBtyEEt7GXkDNROIwfP14/l+cxcOBAVdiIQapevXr6HCKBf/zjH9xxxx1AyHD00EMPcemllwJw3XXXAW4dLxtdMVhIH+vfv786PKV+8vLyVMklBoz//e9/argQx2peXp4ariKF//u//9Own02bNgHumderVw9wjltwbUlUde3btwdC6roHHniAu+66CwjtM9LT09WoKOFHxhh1Los66N133y2SAWrv3r2sWbOGNm3aqPFEDELTpk3Tti8hVz///LMquERtcvLJJzN16tT9OHTu3FkNUmKwkeiDypUrazuTNrhnzx4GDx4MwAcffAA4A4j8D6m33bt3a3hUYZCcnEznzp1ZvHixPre//vWvgHPOi8H5qquuAlzfkj2ZqLZyc3PV4Px///d/Wg7pZ2LYkTIOGjRI60DawMCBA/Vv+V/Lly/XZy0qqj179rBqVdSFhz4hsIeHh4eHh4eHh4eHh4eHh0c04ZUzHsUKkbDt2bOH2rVrA6EkVN99953KQiVO+l//+pfGTUq40llnncXtt9+unwOMHDmS119/HUA9RhK3OHDgQPVKiDV46NCh/Pvf/wacxBmcSkdkbWLxnz17tkprPTw8PDw8PGIT4kndtGmTqjMkvPqdd95RlUmsYciQIUAodGLChAmq/v3kk0+AUHgCoGEB0YSEUkhouihNzj33XA0tERVMu3btNBxGeOTm5qry5JxzzgGcAkNCEiKB6tWr6zOWfDjWWg37kbwbo0ePVi+9rHPFKz9y5EhVDUmOjTPOOENVA+L5f+aZZ/R/SbLga665RtUOkcAbb7yh611RA3388ccMHz4cCKmWkpOTNYeJ8JKypqena+JVUT30799flQ2Se6VcuXJapxKK8sknn2gfjRRWrVqlChAZA26++WZVzMh+YNKkSRr+JDwk78itt96q4TwSarRr1y7eeOMNABo3bgw4hZGMKaJmGTNmjPItDHbv3s2CBQuoW7eu1r20+3feeUdVHOGhRBICKM+3SpUq2kdkDzRv3jxVvYiiTvZY27dvVxWeXD9o0CBVj8ierGHDhhrZIAqiTZs2aQ6swiA3N5fNmzczdepUDWuS8alXr16aa1TGsw0bNujYsWzZMsC1USnHe++9B7goCelnixcvBkJ5SJ999llto5JTZ8+ePTrebN68GYCzzz5b/788v0cffVT7RjThlTMeHh4eHh4eHh4eHh4eHh4eUYRXzngUK6pVq8aQIUO47777GD9+PBCKG2zWrJlapiWu95FHHmHgwIEA/Pe//wVcIjY5EvPaa6/V6+VYNVHMSLKouLg4TdAlsa2ffvqp5pqRGMwdO3aoJV28L9dee616LSRHjYeHh4eHh0dsQPJdSB6NvXv3qndU8kk0adJEDycQRW6soGAyzK+++kpPoRQv85dffqmnkZQGSM4fyeshp3cmJCSoikbUGHfffbdyk7Vh/fr1ueaaawBU8WytVTWK5DYpTrRt25aZM2cCocSx1apV46STTgJcsldw+Wgkea543p966inAJR6V3IdyKsz//vc/VTFIPg1jjJ5CJcrvjh07qqIhEnjssce07cuzz8jI0MMzJC/QlClT9DQnUZBIbo4nnnhCc6+0adMGcP1NTquSI+z37dunyZXDjzAOP9I+Eli/fj133333fuVbtGiRPndph3Xr1lWlyNlnnw2E6mbBggXafsOVPpK7Ra5bvny5JqWVBMKrVq3ShLeFQXZ2Nt999x2zZs1S9Y0oQNLT0zVJ7yOPPALAm2++qbmaJHFv5cqVVXly5plnAtCjRw9Vikg7k+t3796t0QuiVNu8ebO2V/ldrVo1VZFIQuA1a9boyVeFwZ49e/jhhx8YMWKEtiXpO1OnTtU2J2PCjBkzVGVWp04dAO655x6tN1Hw5Obmar8UdZ7kblqzZo0mGpZ+3KRJE80ZJTm8LrnkEs23JBEZl1xySanI3eWNMx7FiuzsbBYvXsyoUaM0IZNMpomJidrpHnroIcCFF8lALwmvlixZon/L4NK9e3eV+slkKMaZOXPmaIKyFi1aaDnkf4kUdePGjSoHlOuqVKmif3t4eHh4eHjEFkTyLnP5hg0b1Okim9DKlSvrWiTWIJs2WVM1bNhQjU9XXnkl4DYwEkIuCT2jCUkGK04v2UhNnjxZQw5ks3TsscfqJljWaKeffrrWp4TbzJ49O6JhMY888ogePCHGlP79+2sSX0mY+9prr+nR2G+//TYAr776KuDWsWI4ksS0GRkZagiQE0sHDhyoXMSI8P3336txKhI49dRT9ZlLAttly5Zp0l/ZyP7hD3/QcCYJRZFyd+nSRRNri7MzKytL26iESM2ZM0fDbsLDaWS9HylccsklGjYj9fDXv/5VeYhRMyMjQ0OxJHRJjq1OS0vTcCVJp9CsWTNtt9OmTQNcuJoYDcQIXBTDDLh2lpKSwsyZM/UQFennN954ozqZxXmdlJSkoU6SnmHDhg385z//AUKGlY8++ug3J7pJ287IyNCQIKnDypUr6/Hhsmd69NFH9SSkhx9+GHCJ1YtyYpokPP766691LJDTcnNzcznllFOAUL+Pi4vTvZsYBGfMmKFjgHDq3r27hqrJSWKyX2zZsqU+DzFaLV++XNuw9N2ZM2fqoTNipLHWRjxpdWHgw5o8PDw8PDw8PDw8PDw8PDw8ogivnPEoVpQrV46UlBTmz5+vXgNJrHbLLbdw7733AiF57rp16zQkSRKVHXPMMXo8nSTBmjp1qkp7K1WqBIQsqFWrVlUrv3iT4uPjVYIoErw2bdqoZVXkz1WqVFEFjtzPw8PDw8PDIzYgUndZGyQlJWlotHh+v/jiC1U3xBpEGSQe5datW1O/fn0gpExZuHBhkb32kYSEjosaQJQoxx13HDt37gRQFcWuXbv0cwl9X7FihSY7lrXkwoULVS0QCfTu3VvDcyQEY926daqckbCQNWvW6NpUkqOKSqNt27b6XQl1adGihSoD5OjtDz74gK1btwKh44z79eun70UCDRo00GSvEn5Ur149PWZaODVp0kTVGaJ6kbV1UlKSJkgWpU29evVUbSCqkvz8fK0/CTucNWuWrvcjhXHjxmkoTvix5JJsVxQ0n376KTfccAMQ6ldyzcqVK1UxIyFMAOXLlwdCyZTbtWunyilReTRu3Jjnnnuu0OUtV64clSpVomPHjnpf2Qv98ssvGoIjqp23336bkSNHAqE9yzHHHKOKK+G3evVqVUSJIkbUbLVq1VL1k9QRhFQ00gYvuugi3b9JtMMnn3yioX+F5ZeSkkJSUpImw5b+XKtWLQ0nEu5Vq1ZV9Y/s13r37q37Pwm3++mnn7T9vfzyy0BI2bZ06VINLZSQrg8//FCTVYvy56STTtJnI/vA119/XZU10YRXznh4eHh4eHh4eHh4eHh4eHhEEUasZx4lA2NMoR64HOt12WWXaUyjWAlfeukljZ2TONYSwjfW2o6HuqBcuXK2YsWKtGvXTsstVvnZs2erdVKSni1YsIClS5cCLi4U4MEHH+Qf//gHEEomPGbMGD2+TuJDxVK9c+dOjZ+VXDW1a9dWj5K08erVq3PaaacBaJKtP//5z2qF3rdv32H5JTWua+vdOeJQl+yH2zu8Xehrh6ZkFfpagH7DCn+cHcCnH99wWH4Q2Tb65JNPAqF45/DYT/FKyT3i4uK49dZbC1OUcByWY1H5/frrrxr7Lhb/xMRExo4dC8AFF1wAhJL6QcgjKMdR3nrrrfq5JBXcvXs3l1xySWGKEo5i55eRkaGeQemraWlp2m9ExVanTh1Nyi1HHEo//vbbbzUn1Iknngi4Yx6lbxUBEWmj8fHx6nkSr9/GjRs57rjjgNDRmJ999pl6a8STI227adOm2qanTp0KOM/oxIkTC1OUcBR7HXbp0kW9QeLp7Natm6oGZRz9+eefNZlgwUSPHTp00CNFZZxev349zz77bGF5CQ7LLy0tzZ5yyinUrFlTvZWSJ6B58+aqepC6yMnJoW3btkAoNj01NVXj8aWOatasqTkFJDGiJBpcu3YtTZs2BUL5E9q2bRuRPghQu3ZtO2zYMLp27arPXOan1NRUVq9eDaCx/7feequOi+LBzMjI0LwNkrNj/fr16vUWT6jco3z58tqHxfO7du1a9ZYXJ8cWLVrYJ598kuTkZPU8iwezUqVKmudAjjndu3evei5PPfVUAF588UXtZ5LMv169enrErTwvGYOWLVumiR6Fc9++fRk9enSx86tTp4695JJLMMaoIkU81eeee64qC0TxkZWVpQoXab95eXmqcJEEnKNGjdL8CZL4UxQL9evXVw+xXD98+HCdM4qTH0CzZs3sAw88wIwZM7S/SFLh3r17a94G8Xh/8803Ok9LH6pZs6Z6+kWtvH79eu2HMkdIXbZv317VMTJ2JiQkHImq6bAcGzVqZMeMGUPVqlXV8y/zU6tWrVSVIPO6tVaPS5ZcGKtXr9Y2Kv24X79+WncyT0ofGzRokCYUFaXFTz/9FJFxtF69evaKK66gVatWvPDCC0BoLlu8eLG2K1mP9O/fX5Ue//znPwGXuFdynMg80ahRI1UcyLOR/CyZmZna36QvpqamqsJE2nbbtm1VxSHz5bZt21RhM3bs2EK10SpVqtgePXowfPhw7X8yPjRu3FjXLqIS6dGjh857Ur4aNWpo+eT/f/rpp5rr55577gHQhOHh843kLGnSpIm2ZRmPMjIydByX516tWjV93qeffnqh58KhQ4fqvkSe2+bNm3W9IvlVqlSpov1MnnWvXr1U8SV9KzMzU3MaSb4aSdD8/fffa/SCqLays7M1oa4834YNG+r+TBRPHTt2VGXNZZddVqg2OnLkSFatWqXzldRj/fr1efHFFwGX5wjc/C5rGcmR06BBAy2TrFfi4uK0LseNGweE6iAuLk6TCUs/bd26teYck/r54IMP6NGjBxCq02HDhmn5RowYUag2GgmUHg3kEcAYkwcsxPFYCVxkrd126G8V6r6NgKnW2la/915Hin//+99AKJFTOC6//HLNfi6ds6jIzMzUAak4s94bY0hMTGTYsGF60pLIOLt168Zjjz0GoANJYmKibppk8fnEE0/o5k8WplWrVtUFqWz2ZcJ86KGHVEYoC4fNmzfr4PPee+8BLgmVdGIZZN988026desGUKQM5B5H1kYvuugiAP74xz8CoWzs/fv313YgUtTdu3fToUMHIDInMxwOwm/KlClaNllkdO7cmZdeegkILdgqVqyoE5hMeLLA6dq1qy4OnnjiCcC139LA75577lGjpUyAM2bM0E2FLNh2796tC1cxutx+++2Am8RlkyL8tm3bFlV+EOI4efJkXcTJ6SYdO3bUJIayAPnqq690XJGEgGIgyM7O1vqVhWCNGjV0XIpmHQ4fPlwT24lh+6WXXtJFzty5cwG38RUZvmyehO+UKVM0ZEAW9enp6RGpwwoVKtC6dWu+/PJLXXTKmCz/G0KbvSVLlvDRRx8B6KL5gw8+0LFCDPbTpk1TY+k777wDhBZsycnJamQNH5si1UZTUlLo06cPWVlZeqKHlGXDhg06LspmcdKkSdrmZCO0Y8cONR5J29u6dauWWfjIJnfu3Lkq++7VqxcAmzZtUuNCcXPMy8sjLy9PDX1Sh9u3b9d6EkPF6tWrdXyR+b9y5coqYZeyzZ07Vxfx0qaFS9euXfW0GQlXWr9+fcTq0BjDokWLdEMwYMAAwBm8ZNMavlETA6OcftOwYUPlKgmK4+LiNNxFIGE+Xbp00TFV2swXX3wRMX5ZWVm89dZbNGjQQNeDstlMTExUo4wY2Pr3769hAPIsPvnkE3VCPPjgg4Cb5yWsQMZYeYZPPvmk1qes13JyciLCMT8/n927d5Ofn69rzyFDhgDO+CT/X/ri4sWLufzyy/fj3KhRI/7yl78AzpkHzrAv45ZsEIXf999/r5ti4TRx4sSI8EtMTKRevXpMmDBBTxwSI1GjRo3U0Cf9f9y4cboGkzXN7t271ago67HvvvtOT8CR5LAy53/wwQfaP2Wz26xZM10HSVLqpKQkNZqL4+bll18uclLu6tWrM3z4cCpWrKjGdeF4xhlnqEFB7rtw4UKdN+S6zp0763fFeREXF6dGC6lfSXfQt29fNdyNGjUKcHum2rVr7/fsvv76azU4y///8ccftQ8VBikpKZx44om88sorOqbJ6WSLFy/WdZU8w6ZNm+o6tHfv3oDrU7ImkffE2A2hdUL4ibRy6q0YVgcMGKBtXvpzkyZNdA0hc9fcuXO1TxcG+fn57NixQ3kAOjYsWLBA1yvy7I855hi9Vp53s2bN+PTTT4GQUyktLU3XNTLni9Glbt26GnoldbV169b9QpfA7VPkfuLomTp1qo7H0USshzVlW2uPD4woW4G/RbtAHh4eHh4eHh4eHh4eHh4eHkVBTCtnCuBLoA2AMaYS8DZQDUgAbrLWvh0oYt4HZgLdgbXAIGtttjGmAyBZnKbJTY0x5YHHgY7APmC0tXa6MeZi4EwgGWgG3AckAhcBOcAAa+0RZ/YSz0vbtm3VmilKkHbt2qmnUKyla9asUU93Qezbt0+TeYllHEIekuK05Kenp3Pdddcxbdo09dDKEWwXXnihegnFCi/WfoBnnnkGcJJnkc2JvKxu3bpq/RerqshJ77zzTvVeiHT2/PPPVzWNHNsWnsRNPHJbtmxR66xH0XAkbVSUF+KpkLCM8uXLqzdRlAjbt2/XI/KioUoQfp06dVKp6OOPPw64IzfFoyDJqJs3b67tS9qmeF0mT56snlBRiM2ZM6dU8OvRo4d6JSTM59RTT1U5qODrr79Wj4p4OSTZY82aNdWjKvfYuHFjVPlBiOOIESPUqyLKuwoVKvDhhx8CIe/umDFjVK0n6ifxuOXn5+s4Kt9r3759qajDYcOGqVdMPH5nnXWWej+ln3399deqEhKPn4yZeXl52mfFk7x27dqI8CtXrhzVqlUjIyNDQwDEq7Vy5UoNWZEQHQh50WSsHzFihIbZiYd29erVGm4h3t1HHnkEcGo9UdpI37XWRqz+tm/fzvTp00lNTdV5TLx0iYmJGuYo/eX999/XxLaisktJSdFwCAmRqF27NllZLgRWVKHiPZ44caKGcckzbNiwoXpii5Pjli1bGD9+PLt27VIFj4wZqamp6n0MV5nJHC6KwoyMDPVwivIgPz9f24S8JyqvE044QcNJxVM/a9asiNRhXFwcFSpUoEaNGurdvf766wH3bOVwA6mzVatW7ZeME1w/knWZjJ1Lly79TQJS8fDffPPN2n5FwVGuXDltK8XdRnNycli5ciVt2rRRz7woC5ctW6b9T8KPp0+frnObtK/s7GxVB912222Aq18ZN4WrhCufe+65Or4I1+zs7IjUobWWnJwcEhISVG0gz7V69eqqRhOl9sMPP6wKPemXeXl52gclDGbjxo0a9iR1J3123bp1upa54447ADfHRqIOc3Jy+Omnn0hPT1flhqiBdu7cqWsTqZ8hQ4aQmJgIhBRt27Zt07LJeNKsWTO9j8z1wrdnz566jhMFRHx8vM47ErL4008/6XOQ/t6rVy+db0T1cDhs3bqViRMnkpOTo+P96aefDsC9996r/0/GnjPPPJN33313P967du3S5y5zQJ06dXSPIsl0RUWamZmp86mMRVdffbWOu6IQu+WWW/S+En60atUqXQcXBrI/S0tL0/8pY33NmjV1zpBxdOTIkVo3Ms5Ur15d53MZb15++WXd70kfFEVRjRo1dEyVI8A///xzhg4dCoTa9JQpUzSMWEJtTznlFA03Kgzi4uJITk7eL2nxlVdeCbjwV+En89aAAQN0Ppfx//3331fFk7SBTZs26V5B9oQyD+bk5OgzFGVjuNpRVGwzZ87UcUlC4G655RbuuuuuQvOLFGJdOQOAMaYc0A94J3hrD3CWtbY9cAJwvwm1pmbAo9ba44BtgFgHxgFXWmvbFrj93wBrrW0NnA+8EBhsAFoBg4FOwFhgt7W2Hc5Q9Idipunh4eHh4eHh4eHh4eHh4VEGEevKmQrGmO+AusAS4H/B+wa40xjTG8gPPq8VfLbSWiuBd98AjYwxVYGq1toZwfsvAqcGf/cEHgGw1v5gjFkFNA8+m26t3QHsMMb8CkwJ3l9IoOI5Unz88cf7/QYX6ykQi6JYPefMmaPW1ILIzs5Wz4BYJ1NTU9XqWJzYvHkzjz76KKtWrdIkduLxbN68uZZbks+1b99evdZizXzppZc0Dl0Swq5Zs0bj1eXoN/EAV6xYUa8XK+m3336ragDxmFeqVEm9Q6LMyczMLOmkymUGR9JGJWZbvB3iXf3mm2/U+i1eh3r16mlcbDQgvLp06aI5ICTOvGfPnupdkPZlrdV2KFwkQWb9+vU12Zx4a4YOHRqRPlhYCL9rrrlmvySC4DwKw4cPB0Le/vz8fPXEiEJIrhk8eDBjxozR74LzzMnziBaEY4MGDTR3jngY4+PjNcZbkiNWqFBBVVLSlqX9GmN0rBKlQLt27UpFG73yyivV6ycqjVmzZqnnUjzUW7du1XFUfkv9du7cWdu35GTp2bOneiGLE9nZ2SxYsIDk5GRVG4i6IyEhQcd2yeFxxhlnaD4TiRc/7bTT1Nsl3tiaNWtqrL3Ui8xD3bt31/Yt/BYuXKgKm+KGKC8qVKigXmcZFzZt2qQeV6m39u3ba+6Yu+++G3AKLWlrkhsgPj5exw3J+/Hmm28CLreAKBYkx0bVqlUPujb4PUhNTeX888/fL7+CtMcdO3Zw5513AiEP+ejRo1XFJH2rU6dO2ubEm5qVlaXKtEGDBilncJ5wSWgqntRRo0ZpUsniRHZ2NvPnz6ddu3aMGOEOAbjqqqsA92yFt9RZ48aN1TMrc0OlSpX0eG9ZZzzzzDPqmRUVkKjGRo4cqd8Vde+SJUtUpVLckLxIffv21b4m48Ipp5yibVQUFW3atNG+KQmRW7ZsqZ9LfoevvvpK852Ip1v4DBkyRD35Mp5mZ2dHZC7ctWsXc+fO5eSTT9a+L4lHzznnHFUUSm6d//znP6qYkZxyw4cPV7WQ5HNKTU3Vehc1uKyxw49eluDZTpYAACAASURBVHm1adOmqsQsTuTn57Nnzx7atm2reW4kn9+QIUN07S0qmfT0dM0JI+VNSkrSvGriw87MzNT1m3CXtUyVKlVUpSFt4LbbbtO+ITzr1aunc4vU88qVK7WNFBY1a9bUw0KkLKL6+OKLL1TNJGuT9957T/uhrEmeeOIJHWdkbF2wYIEmvxa1mIwzLVq00D2C1OHSpUu1zkVJ9u677+q8JSrVc889V/8uDHJycli+fDn79u3TZy7qXQgpZkQp2KFDB+Uqqo9WrVrp56LuWbFihaq0ZX6Uufahhx7SeUVUTcnJybqukzVSt27dlJ/UW/Xq1VUFXhjs27ePLVu2MG3aNO1vMv+mpaVp2WT8nzFjhkY7yNqjffv2qtwRNd/dd9+tY4q0W8nr1bJlSx3PZA3Uvn17XRtJu+3bt68qqWTce/zxx48kgX6xI9aNM9nW2uONMRWBD3Eql4eBC4EaQAdrba4x5mdA1C45Yd/PAyr8jv8ffq/8sNf5RPjZSicKP6M+fJNcEBI+JAP4woULjyQ7/mGxb98+fvnlF1q0aKEdRwbm8847T+XBEnJ011136eZWNuWNGjXSwU02dwsXLtQJRgYL2dinp6frKUASRvLiiy9y6aWXAuji6IUXXtABSYw4559/viaOFCmdR/HgQG1U6k6ktTJhpqSkaELmc889F3ADdyTaaFFRv3595SCJDN98802dEMTQNG/ePG1rMuBL+23durVKvgcPHgy4TXFp4Dd79mw1ZMrk3bRpU90EycahYcOGKnEVo4aEeb3//vsqG5dJ/Nprr9XwtWgjLy9Pkx5KKJC1VtucLLb27dun0lhZLMi4U65cOdasWQOEwrmWLVtWKupQxjMISe/nz5+vY68sbHbu3KltVBafktB71apV2mdlg5GZmRkRflWqVOG0004jMzNTF/OyINy9e7fK7GURmpycrItImSc2b96s9XDLLbcAztAhBngxzoQnGJcNoGyoWrZsqSdYFDeysrJ44403uPDCC9U4Jny2b9+uf8tceOGFF+omQRbnycnJemLOAw88ALikxyLhFz5ikNm8ebO2ZTGEzJgxQ5N0Fyeys7NZvHgx9evX1zYnSbf79u2rY7u0pSlTpmgflI1Et27ddJ0gJ1U1btyYG2+8EQgtogcOHAi4ZI1yvYTgvfrqqxFpo/Hx8dSqVYt9+/ZpElk58Sc3N1e5CveGDRtqvcjcMGnSJDXsiDEnNzdXNyfSDqVt79y5UzfKclpT69attS6LGzt27ODjjz9m+/btul6STfjs2bPVqCfzwzHHHKPzwaOPPgq4JNAyL4rxsX79+rrpks2RtFFrrRoNrrvuOsAlGi5smEtRIAdULFq0SLnI5nXMmDE65kg/6tOnj7ZDMZru2bNHnZpiIH7qqac0vFLasqxHzzzzTDV8yDz5+uuvq8GxOJGdnc2iRYto1aqVbiilX8THx6vxIbx/yrgjTpQhQ4ao4VDW4gsXLtQEqfKehOA999xz2h6kHVevXl0NdNJXxo0bp9+VMm3btq3IYV0SuvXaa69pcmJZh9SoUUNPGJQQpj59+uj88dRTTwFunJe1pxh4nn32WT20RL4rBkUJ24JQvW7dulXHLwkX69evn4YiyXPdu3evGnkKg7i4OCpVqsS8efPUESQOorS0NDVkSrjdX//6V8aPHw+EjIqrV6/WRM9ikGrZsqXykn4pzu5atWrp/CgG/uHDh+u+S8o/a9YsXTtI2+/UqVORwpri4+OpUaMGGRkZ2h4l9Grt2rVqgBfOQ4cO1XFUnBrx8fEaiiyG6saNG6shUhwX4kRdvHixzhPSjz/88EN15sh49eOPP6rTTv7nrl27NPQxEvNmYVEmwpqstbuBkcDVxph4oAqwKTDMnAA0PMz3twHbjDE9g7fCzy38XF4bY5oDDYClxUzBw8PDw8PDw8PDw8PDw8PjKEWsK2cU1tp5xpgFuLwwLwNTjDELgbnAD4W4xSXAc8YYS1hCYOAx4PHgXvuAi621OUWxHEYbNWrUUImzWFBvu+029SAUJ1JTU7nggguYMGGCer1EAhduORXLZUpKinqNRF6en5+vVm7xNpx88skqxxSvi9z/nXfeYdKkSUAobGvTpk0qp73//vsBJ/cUy7iEmFx99dUqxxw9enRxPgqPA0BkruL9lnqYM2eOWuvFIxee+DKa2Lp1qyoQRAlTsWJFlTiLZ6Vu3bqqYJDEayKVfP755zVBmfTBxMTEUsFv+vTpqiYRWe0TTzyh5RWPZ1ZWlspSJcm3KC2uu+6638iV69atWyr4gRtvxMMl3ryZM2cqb5G77t27V8cjUSJKuGXTpk01kaV4hd9+++1SwfHRRx/VkEHhN2HCBFXJiAz8zTffVEWUqDPkqOaKFSuqykFUAampqRHht379em677TYGDx6snjPp9+np6eqZEy9nYmKiesekL4Z7o8Wbn5iYqF5V8WxLmMK2bdvUey6fZWdnR6z+KlasSNu2bRk8eLCG8ci8d+ONN6rCRZIwJiUlqcJGknBu27ZNJd7Cq1+/fhqiIV5o6ZedOnVS1YkkqGzRokVEOCYkJFCnTh0+++wz9WaKamnlypXaHp9//nnAKdBEmXfFFVcArn4LjhtbtmxRT7uoEiSR5MqVK3UOkTpfsWJFRPglJibSoEED5s2bp0pP8US3bdtW60g4LVmyRL2/Evbxpz/9ST3Uffr0AZySRDzfogiStr1gwQIdp8RjXqtWrYi10erVq/PnP/+ZjRs3qpdY5oBatWqp91nmvcsvv1yVQMJx8eLF2ifDx06Z26WPyri0fft2XYdJvW7evDkiHGvUqMGll17K/fffr6pHSWJbuXJlXSPK8cOff/65HmUu6ohp06bpPCd1kpCQQLdu3YBQPxMF4PPPP68KC5nrr7vuOl577bVi55eSkkKvXr14//33VcErys/58+drkmVRxpxxxhk6xsu8vmfPHv2OjMXXX3+9KkFEURCePF8UCLJmu+iii7TuBT179tTP5dlXrlxZ23xhj9QWBVuPHj10bBNV67HHHqvjgfTNLVu2aJ+TvcIXX3yhazJJ4N2hQwdN8C+qDHk2aWlpGk4k5czOztZ5X8JnKlSooH1DVCfVq1fXtlMYyFryiy++0LFQ1KxbtmxRZYe028mTJ+szlHGpQ4cOvPDCC4BToYFTFIoSU+Z82VfFxcXp8doSbZCVlaX9QdQ0zZs356233tJnAk4tVZS+Gh8fT7Vq1ahQoYKuj2VOfvfdd5WD7BMnTZqkocly3d69e3X8kPGxV69e2q4l3KxHjx76TOUe4cnnRUUjyqiePXuqMlXUMpmZmbrOiyZi2jhjra1U4PXpYS+7HeRrGmBurb0v7O9vgPBkwNcF7+/BGW4K/u/ngefDXjc62GceHh4eHh4eHh4eHh4eHh4eB0NMG2c8CocrrrhCPSBi8RSFSXHjl19+4ZlnnqFSpUpqcRaVxMqVKzW+X6yUy5YtU8uwqF+qV6+uXoAuXboA7thUsQhLsi+5/4YNG9QTJVbVVq1aqTcqPP5bctOIByQ1NVW9kB6Rh7Q7sdCLd2bp0qWa3Ew8+ampqZqUL5po3LixegYlvjk8Fli8KRs2bFDPi3g+RHWRmpqqx3bKkarTpoUL9KKHjh07qidMjo2sV6+eeszEk5qWlqZ9SjjIM2jatKleL150SdxaGrBmzRrNKSWetu7du2tCY/Eav/rqq5onQJRDUr/vvPOOjiXyvdq1a6vnKZro1KkTTz/9NOC8+uCUBaJGFK/X1KlT1YspHj/x8u3atUvzs8g9ItX/6tSpw4033sjMmTO1DYkypHv37lpXonRp1KiRejxl3D/99NPVMyhjx6RJk1QVI/nVRI3Srl07VVvKPaSOI4H4+HhSU1N5/PHH1QMo+c0GDx6s6gHJ27B8+XIdI8QDuGzZMlXoiddxzpw5mgNL5kfJT1KrVi1VNEg9y1hb3JCcOk2aNNE+L3WYkpKiyW6lz/Tp00eVMJL7bfz48aockrnhz3/+s+Y9Ec+3qBNvu+029fiKcuXkk0/WfBfFiYoVK9KmTRvatm2rfURyPMTFxWkeD+Fevnx5bXPiiU9OTtb1iuTFCVf1ShuQMaZcuXKcdtppAHp8s4y/kcDWrVt59dVXOeecc3SdJl72JUuW6DOWXBg33HCDtldRW4wdO1bzXIh3/9JLL1WOkntIVBlNmjTRPimqk1GjRnHxxRdHhN+ECRMYPHiwjikyBixfvlzHD1FXjx49mnvuuQcI1cnxxx/PuHHjgFCOtQcffFAVYfLcJIfXH/7wB811IvNkpPKSyVHoXbt2VWW5zNHz589XVZdgwYIFut6V3HedO3dWJYqo0RISEnQtLQoEqe/U1FRttzI+d+vWTcdxURmlpaX9ZgzesmWLjoWFRW5uLhs2bCAuLk7bmaw1li5dqioW+f9NmjTRssv6a8WKFTqWSF8eNGiQfldySYmK/o033tAcUaJ2XLlypeaIkjnmhx9+UGWGKDtWrFihbaEwkLxP/fv313Fdcuv88MMPOqZK+VNTUzU/k8zXv/zyi+6ppH1XqlRJ5wVRSYmy7+OPP9bjwKWffvLJJ6o+uuaaawCnzJRxTpS2+fn5qgwrDHJycli1ahWNGzfW/afMCTVr1tTxQcohikz5X8JPoi5EVdmiRQt9XrKWkXFq165dupYVFcyIESO07Uv97du3T9u+jA/GGG1f0YQ3zpRhyEQpSXghNEFGSrZVvXp1LrzwQsqXL7/fxg2cUUU6oiy2UlNT9dQFmZxvvvlmHYhEhnb22WfrQCTSPJkEGjVqpEmr5Hu7d+/WDiYblGeffVYlbCJBvPPOO/c7YehwiMuOI2l+xUJff8OvQwp97ffdinbqS+Ivuw5/USmDyAnlt7TR6dOn68ZE5N+SrDXaEGnwkSAW+N13332HvyiALACkv8nidtGiRToxSv+UzWFpgCRjDkf4hk6MLRA6DUAWMrKYzMnJUf6ywCvKqQWRhCwuwSXxE4jB+0DXSlI+qcukpCRdnEsS5LFjx6pRvDixYsUKDdcpCNkQHikkLEE2zbIAT09P11N9ZNEuRslIYPPmzUVKKHjyySfrnCWbhMTERA2XkBC1Bg0a6FwpBg0Jn1i6dKmGsMnzlUTdxY1t27btlzjzcOjSpYsm6pQNfm5uro6Dkgj3pZdeUqm7hBbKpmjevHn6t4TEiPGjuLFt2zamTJlCmzZtdN0iG7WFCxfq+kMMfFWrVlVjg6x90tPTdbMsm/iuXbtqMlYZgySMYPLkyRqqJglYJVQsEpADHHbu3KlGa3FgPfzwwxqWIp/Fx8frpl7CdJ599lnd1ElS2tatW+v4L9d99dVXgDNwyOZONtGRaqN79uxh+fLldOvWTdtX+EmDktj273//O+CcE+IslA3iiy++qMYICaO48sordb6TupR17vjx47WNyjOQ8JnihrWWvLw8mjZtqptnmZtGjhypG3V59u3bt9e+JfNErVq1fjMWd+zYUfnI+lgM9lWqVNH7SX/+/PPP9XS2w6Gop//l5uaSmZnJli1blJuU94477tByyoa/WbNmylsSxPbr14877rhDvwOuj8pJfhKCKWPnihUr1MEmcyGETrgSw80rr7yiY7zU9YwZM9TgXJjxsXz58mRkZJCZmanGJ0kSX7FiRZ0TpA1u3bpVn4OMEZs3b9bxRX4vXLhQkwTffPPN+/Hs2LGj8rrgggsANyaLsUM+mzZtmnKRMSAjI0P7RmGQm5vL2rVr6dWrlya1lzk5KSlJx8rw8GMppzyHypUrq9FUEiNv27ZN19di/JN22aVLFy2j1OPTTz+toYiyRl27dq3OnWJcy8rK0j4STZSJhMAeHh4eHh4eHh4eHh4eHh4esQqvnCnDkKRRCQkJ6oGSRFmRwo4dO5g+fTrz58/XZJviFUlMTFRPs3inv/nmGw1hEq/mtGnT9Ag1kQdWqFBBj/YTSZp4tgcMGKCyU7Ge79mzR2Vy4rm6/vrr1Usn3p9hw4b9JiGhR8khGm20JFHW+IkHUI5VXLrUHVxXr1499YKKh62o8uXSAkkOKWE04g2uVauWJq0VD3lR5L2lBSLfFe+RqC/atWunHnAJyygNifGKCgmnE4+geBInT56snt7wpIKlBccee6yqYyT8Z+PGjRpWIArTKlWqqJpG5kBJCLx69Wr9W+T2vXv3Vkn4/7P33YFVVVn366YnJKaRBBJKKAmQgARQOtJBpMOIBZVh+KGCZewyn9hQB0c/7Kgjg4MOVRCVIkWaIE0QCIEAoSYkkABJCKS39/vjudYLMp8mTp4hzln/EPJu7rvnnnP22WfvtdepSRw+fFjlZGSKPPjggxqHXP+3b9+ujDBZW8xuBgYGaoxyDlJ0vbphWRbc3d2RkpIim00RZ09PT9k3ZtMrHhHNbPfy5cvF3KXv8+233+qZ6QexTCEwMFBsYV7D42+dgYCAAAwfPhwXLlyQHeCx0vHx8WKR0IcKCQmR+Obo0aMB2MU4ybBhn5w4cUIsBNpPlsAEBgaqz8kmffbZZyU8Wp0IDQ3Fgw8+iMTERNkBzrENGzaorWTVDB06VKwMlsq7uLiIzccynTFjxuh5yTzkOIiJibniPQCO48SrGy4uLvD29sbOnTvFrGDZxyeffCLfmkdGL1y4EJMmTQLgKF9bt26d+oGM8vPnz6v0kO+NDAQXFxeJ65LNTkF9Z4B7iujoaLE9WcI7ceJEMTpo71988UWxY9hvrVq1wquvvgrAUbqVnp6usl2OTX4WHh6uNZCsuZtuuknl7Zzfffr0kZ1l+WlaWprYZ5VBWVkZcnJyEB4eLjYgn8fHx0fldbThJ0+e1P6F8+jbb7+VeD6ft27duvI5X3zxRX0X78tySbJN2rVrh/79+19xX5vNJrYYfxcSEiKWSWVgs9lQWlqK7OxsPPDAAwAcLJzly5fL3+Lvvv/+e+3JyGDx9vZWW7h2LF26VMxDrhPc//n5+el9TZ8+HYDd1nDuc60ZM2aMrmM1R5MmTTTOahKGOWNgYGBgYGBgYGBgYGBgYGBQgzDMmd8hWANN9klxcbFqDhlddxZ8fX3RuXNnhISEKHPCf3fs2KGaRmaKbrvtNkXrGXk+c+aMslLMOs2ZM0cRTmbRGMnv1q2b6m0ZGT116pRqURktXrRoEebPnw/AUSdcXl4uwSvWPho4HzU5Rn8L/F7bR8YMmQcVM6psM+exs7QgnI2fshKoAdGuXTvZHDL6mMWvTaDOCuu+b73VrouVnp4u4XgK8dXGPiTrgowTtjc9PV1aEMwGX0vo3r27xHzZhrFjxyqzSUHO+vXrXyGICTgEkdPS0qSzwPp+CiPWNAICAmQbyAJas2aN1l/aylatWikbTTvDrPjf/vY3sZ2oM0DWRnWDzJmzZ8/K7lGnIy4uTgwS6k/06dNHmW/qHeTn58sPIZvC1dVV9oOMDWrwhIaGip1IAeiuXbvKR6pu2Gw2FBUVwd/fX3Od2eri4mIJeLLfPD09xT4mG6Njx45ia5B907hxY/zlL38B4NBpIQsvPDxcY5msE9qi6kZeXh527twJy7IkJE3B5Xr16sn2cR5t27ZNaxq1uV5++WX5l9SqOnDggFiTZCxQG6igoEDsBbK82ZfVDW9vb1x//fVYvnw5xowZA8DBYomMjNQcITPmm2++kUgwny04OFj9RiFyFxcXredcC9jO3Nxc9R/HNnV8nAVXV1eUlZWJ7UHR/i1btmjcUhune/fuWpf57Lm5udpz8Nlnz56NP/3pTwAgAXK2MS8vT3aG+405c+bgscceA+BgGIWFhYlNReHedevWiT1c2Xnr6uoKy7JkD8i4mjx5suwLWUBDhw4Vc4XvPSAgQPOL690dd9whth61W6i5EhAQIJZf06ZN9axknvD97dmzRxULRHFxcZUOCggLC8Njjz2Gzz//XDaQfTZx4kQdj069nSeffFLMFvpbCQkJEtemjk94eLhEuanBw3UjMzNTejvPPPMMADvTj+/r3XffBWBn9XGvSb+8Xbt2Wk9rEoY5Y2BgYGBgYGBgYGBgYGBgYFCDMMyZ3yGeeuopAI5sx+rVq38znYu8vDx8//33OH/+PCZMmADAEZUuKCjQCRqM7t58881Sb6c+x6xZs5TJ4OlSderUUQSbmQ1Gj9977z1FjRkVf/311xUxZYT47bffVs04jzR1c3PTfQ1+O9TkGP0t8HttH5lszFZXnIs8rYDtZP12bQNrvJmlY2YpJydHLAa27eabb77ipKTaAGZMyQJgfXtwcLDsIjUF2L+1CcwC81Q0ZsvatGkjHQmuNcxUXgtYsmSJMq2s7y8sLNT6SA03Ly8v6QbxKG3qR9x44406wpp6EM5illQVW7Zs0aloZFMEBQWJHbNt2zYAdhYFM+N8DxyXPDmE1wGOU7mqG7m5udi+fTtCQ0PRuXNnAHbdIsCuB8MTs/i7Y8eOaTwxyzt06FD5F8zWBgcHX3WaD1kKx44dU9aWbBraI2egvLwceXl5cHNz01HMZF507txZDDOyeU6fPo1HHnkEgIMRffjwYbFtOOe2bdsmrRmyjnjcLXVSKn4X2c3VDcuy4OLigtjYWJ0syCy7l5eXnpHsBDc3N+zduxeAQ49q3rx56lfqygwaNEjsAupYUU/J29tbDAz2pbPmoLu7O0JDQ3HTTTdpTnHex8TEaAyR+XP33Xdj/PjxACA70bJlS+kzkmnSoEEDvRPOM/6/vLxcbBEe1Tx8+PB/eyJidSAwMBAjR47E5cuX5VuQ1RoREaF9AOfQgQMH8P/+3//T5wDw0UcfaRySxXT77bdrXJNhwvEeERGhE4uodTJ58mSxaMlEcnNzU99yjGzevFlHrlf2NLufMlX5/IsXLxZjhs+/e/duzSUyUQIDA8XWoh7NiRMnpMnCPRjt6LJly9C7d28ADjtz/vx56RFxvLRo0UJMMzKPMjMzq8SoPXv2LF555RVERUVJS41+5KlTpzB79uwrnjE/P1/vl30aFxd3hWYVYNeWo74cGVwc+0uXLsXkyZMBQDo3q1evlp168MEHAdjXT2p80f4dPnxY62pN4tpYtQ2qBXQ4SQ8jddhZRvPfwc/PD/369cP27dslqEnna8aMGXL2uWl97LHHdFQxJ2njxo0lZMYyKB8fH5UdcXLSsIaFhUlAiiJmsbGxMgD8/rVr12oxJiV12bJlMgoGzse1MEadid97+1giwfJBirmdOXNGziznJxfK2gbaFTqApPVmZGSIKktHrDbaDjo+LBVZu3YtADutl5tnbpqulaPCq4J77rkHgIPiTOe9bt26ojDT8eZG7FpAQUGBNvV01o8ePap5xd9lZGRo/eL6WLGkgU48S3d53GhNo6L4Itf8jRs3aqPBjf2uXbt0FC4DpBVFPFnSxZIYZ9mZkJAQTJw4EUlJSQqi8F22aNFC/gg/a968uQIA3Lhv3bpVNpKisGvXrlVyaujQoQAcpRNr1qxRaQE/a968uQJA1Y2CggIcPnwYN954owSIWZ6zbNkyBU24GfP09FQfso+GDBmiMi22Y8CAAZp/3LzS7pw4cULin9z4Oausyc3NDaGhodi3b58SeBRhjo6OVrCdAqCnT5+WD8lAaaNGjWRDaA9DQkJUesKADcdo165dVcLMYKKzjtIuKirCyZMn4e/vr+AJg4CDBw9WmSTLt2688Ub51vRJXnvtNR1TzGBLTk6O/obXMUnRrl079T2DxBWPm65uZGdnY8mSJWjRooXaQ7sXFRWl+Ue/o1u3bgqcMkkWExOjPQfLPu+44w6VITL4ynFZUFAgO8OgQGJiouY1A3yXLl1SsJJjY9KkSRrrlUFBQQESExPRt29fzTOWtb799ttqK0t8mjVrpuQ1RalvueUW3H///QAcgvi9e/eWwC8TFvRHe/ToocARx0P37t1lS/n8zZo1U0CHfztw4ECVADEp8HPw8PBAgwYN0KZNGwVCGOjx9vZWiRTnz+XLl1XiWlFsnX7YlClTANhtMPuGNobHbN9+++0KjDJoOmDAAGzatEltBezzh4Eovr+0tDSnClxXFoYyYGBgYGBgYGBgYGBgYGBgYFCDMMyZ3wmCgoLwzjvvAIAoiqS0MTvwW6CkpARpaWmIjIxUhoUR6M8//1xZGQpUderUSdlnUupef/11ZZaYvfbz81NGg1FSUhY9PDwkwMWsiM1mk5jd2LFjdR0zGqTt33vvvYpMGzgX18oYdRb+G9pHMFPKzFliYqLKC9asWQPAfnR9bUNQUJCyxSz/Ie23QYMGovKzVPJaOHKxKggKClL2j0xFUn07dOigjCCzwkOHDsVHH31UA0/66xAUFCQmENedinR3CrUuXboUAHD99ddrvNY0fH19lf1mti83N1eZTfbXuHHjxK5gxpf9FhQUpEwnjwQmlbymER8fr5KCxx9/HIA9C8vnI0U/KSlJWXqyf8g+KSwslNAzxVmddZR2Xl4edu3ahWPHjqF9+/YAoBKzgQMHal7QLzl69KiYuSyvPnfuHEaMGAHAwT55+OGH5XPQz2HG/u233xbzgawhZzIQ/f39ccstt6B9+/ZITEzUMwN2BgKz1WQFnD9/XuxniqkuWLBAjLSHH34YgL10i2V4vB+ZXy1atFCpIRkYLDesbpSWliIrKwsJCQmyByw5uvHGG+UvkvGzdetWsbDJzGrfvr18SZa9XLx4EX/+858BOIS3yTZYtmyZjvbl8bxkbVQ3Ll68iGXLlqF79+56xjfffFPPTYYnma5z5swRQ4ACtpMnTxazgmzz1NRUzJgxA4CDsUGh/K5du8q/ef755wHAqTY0PDwc06ZNw9y5c8XY4HNGRETgn//8JwDHmlxYWCgWHtkeW7Zs0TOTWXju3DmxEsmUoH367rvvNL7pB3h4eIiZQcZXamqq2CN8xx07dlSp25IlS36xfcXFxTh9+jQ8PT1VakZGyJw5c2Tjyd765ptv5HeRv88tzQAAIABJREFUAZKTk6M5SvtIWwQ4bCWvadKkiWwwS8XS09Px0EMPXfFdGRkZYkvTpq1cubJKR8NblgU3NzckJydrvrOsc8GCBRpf7J+TJ0/qZ87Zdu3aafxxLlU87pzC12RkJiQkiH3EMq9hw4bJVrNsafbs2fLhyDjq1KmTfKKahGHOGBgYGBgYGBgYGBgYGBgYGNQgDHOmlqNirTKjtRQGmzp16m/+POXl5SgsLET9+vVV78mavoSEBGVkWbu9bds21bsyEzVixAhF9ynmVLduXUVumblh9vCrr74SI4aZrbNnzyprwSP2IiMjdYwgI+qPPvoo+vTpA8DB5jGoXlxrY7S68d/UPh5HyuwFM9s2m00ZELLdXn311d/4SX89KraRjD9mFnn85M6dO8XWo0jfwoUL8d577/3Wj1tlVGwf6+yZNfb39wdgZwgxM0mmAuvzr3VUbB/ryskcYQa4Q4cOyrAxA84xey2gYcOG6gsyO0NDQ8WcYHt2796to4vJymAWf+TIkWoj2QjXyrHhAQEBV2kqnDp1Sll3Hp86ePBgMfOYGSWbISMjQ/oWZNDynVU3fHx80LZtW2RmZkq/gKKRZ86cURaW2faSkhLNGzJJ1qxZg4ULF17RhooMNf4t51mXLl2kO0QmC7X7nAEPDw+Eh4dj9erVsm1kWzRt2lTvmgyfgoIC6V384x//AGAXmWUmf/r06QDsIs3sa2rzUG8iLy9P/UofbvDgwRI0rU54eXmhWbNm8PX1leYM2UBHjx6VFgz90pCQEGnl3HfffQDs/XDLLbcAcGhsZGRkiFlDnTmy3Tp16iRNDWrpcL2obgQHB+POO+/Ejh07xLagxuJdd92ldrEfJ0+ejL///e8AIHHbuLg4zSGyDgcOHCgNkJ+yvN566y19F1kXztTuKi0txYULF9C+fXvZCrJUevXqJb0t/i4iIkLt5r/vvvuutEquv/56APY+5BwbOXIkAAfDOS4uTuwuag+FhoZq/pMBcvPNN+s4Z2qdke1RWQQFBWHMmDHw9fXVUd1keM6dO1d2hgzD22+/XXsqslr69+9/FcNp8ODB6jP6o/Tbjh07prWerKGePXvKX+Veq23bthLWpT+UlpYmTZgXX3zxF9tns9lQXFyMW2+9Vf4jx1S9evXEfKSob8U1getgbGys5lfF48PJgmPbechBRU0ojo+vv/5az7tlyxa9I75X2rDMzEynaWBVBRYbZ/DbwLKsan3hNKBcMAHHCUek4FcjfrDZbDf83AWurq42X19f9O7d+yqq40cffSSqHRfKtWvXyuAxmGJZljYOdGji4+NVVkHjRxrnvHnztNBQXPCZZ57Rz3SAOnbsKEo/HYGsrCw5r3v27PnF9l1nBdk6WX1/7pIr8Mapyp/QMyHx7kpfCwCNrsuu0vWfd/v7L7YPqPoY7datmyigXMgOHz6ssUnq6+jRo7X41eQYrWr7Nm7cKNovNz4HDhwQdZZ02u+++w5vvfUWgNrVvldffVVOCqnnoaGh2vyQ4nn06FEsWLAAQM22D6h6G5988knNfZ4Ek5qaKjtAmmtBQYGovZVt4wsvvADAcbrHtm3b5Fz95S9/0b0YuHvmmWeqvQ9nzZolx5olntnZ2bJtpNz7+fmJ2kzqMGnbycnJcoC4OS4vL9dGk85ZRESENi50UhcsWKCg3Lvvvlvt7Xvuuedwww32W9JnWbVqlWw7N7SNGzfWqT90MLlebNmyBd26dQPgoOF36tRJ5SNEbm6u5jvXpvj4eDl+fn5+ThmjH374oewjaeMRERFyXimOOHr0aCU0WG7DDWd8fLyuI/Xe09NT/cS+3rJli94Zf7dy5UrNjQkTJlR7H65atUr2k7bl888/v+L0FMC+5nMzyznFTbGfn5/6lf7CuXPnrhLv9PT0VACAG5NGjRpp4zJ9+vRqb99f//pXBV0YLDx69KjKtRmkOHjwoK7jxp7/Lykpkc3ghvGOO+6Qv8TAcdOmTXXaCssY9u7dK4r+lClTnDJGZ86cqbHEuREZGal3zUTX9OnTZQ84v2iDvv32W4wePRqAIyE2evRoBYVoZxo2bCg/kEGc06dPaz4899xzTulDlrEySHPnnXfKD2VZy8WLF1Vaw6ATfZ9NmzZJkJb3GjBggNaaDz/8EIC9JIVBEY5fBpIAoE+fPtXevltvvVXjipvR5ORk2QBu5nfs2KE1gJtzzqM6deroeo5HBvYARyAoMzNT6w8DWT179pQ/8ac//ckpY7Rv3746jYdB1dTUVO0fuG/IzMzU+2cwjWN1z549Khli0MDDwwMzZ84E4Ojz0aNHq19pe9LS0jQPb7/99mrvw7feekvPuXjxYgD2IBHfe8WTFrlWsu8YMD106JDEgjlP8/LyZEs4bn18fDQmGFB55ZVX9A7vueeeX2xfo0aNbE899RQ+/fRTreE8jWrOnDka7wxQjxgx4qqEwt69e1VqxcDJpUuXlDhjgIXP7+LiIgkNts/Hx0d2lAmPunXrKojD9Wfr1q3625EjR1ZqjDoDpqzJwMDAwMDAwMDAwMDAwMDAoAZhyppqKRi55dF3gD07DDglm11puLi4wMvLC1FRURL1Y0Q0OztbUUxS6mJjYxXNJBPhzJkzys7weLzevXsr6krqLDPcb7/9tqjspAC+//77uOuuu/QzYI/4kv7G7FurVq0kXMfIqUHVMGDAAFG3WTY2ePBgCT3zWOmXXnrpmhijVcWKFSs0rpgBGjp0qOjmzBoWFRXVyvatXbtWGVzalUOHDmH9+vUAHEdo+vv718r2AXY7QxtB8c3k5GRl8Zhh8vHxqXIbyZJh+Ux0dLSyaRRxXbp0qVPLS1q2bKljNckocHFxEW2fjIHQ0FBlo0mNZlbtf/7nf1QWymNUy8vLlWUiG6BJkyZiQJCtc+rUKTEEKGhanUhNTRXzgKJ/KSkpOm6YfZCRkSEaM8tqyWzy8vJSBp5ZTi8vL7HGmCH08fHRfCe75JZbbsH48eOrvV0V4enpKVFtPt+5c+f0LKSwZ2dni57OjCEFH7dv366SIWb/ysrKxIjiOO/QoYMYCLxHfn6+spjOQEZGhlhbZFG0bNkSxcXFAByZ/IULF2qufPLJJwAca3NQUJAYDbzmu+++05hmptyyLLEyOC+PHz/u1PYlJycrc8vMdqtWrWRHmdkeMmSISoLItqBA5oEDBzR+KQR99uxZlWLQL4qKihLbhmWKbdq0cbqQ5dy5c+VDMXu+e/duPT/LXIKCgmSHmMmvyH7imL77bjtbeOXKlRq3ZPSlpKRoXjO7XVxcDGey/SuyHioyIVmywvbdc889soEskWB/DBw48Kqjsw8dOiRGHwVKi4qK5E9w3qekpMhGOQPl5eVi5/E9lpeXyz7QL27UqJFKuXgd2TK+vr66B4+ODwgIULkJx6BlWVprKwpqk33iLISEhMi2cc2fNGmSynfI2gsMDNQzU8CZ63avXr3ky5LZFxcXJzvEeRsYGCgh85dffhmAfa3gPHUGUlJSNF5o4z08PDSn2AbLsmQjycTkPOrfvz/++te/AnCMR8uy9L6413Jzc1Mfsw8///xzrbuVQWFhIRITE9GqVSsde05mqq+vr9YCspf79u2rMUIx5KNHj2r8kbF25swZfPnll1c8G+duUVER5s+fDwAqFVu0aJHsMr9/2LBhYvjTv5g3b572ojUJw5wxMDAwMDAwMDAwMDAwMDAwqEEY5kwtBWv+mekGHFHimkRAQABGjhyJOXPmKHtA5oxlWWLC8Di7HTt2KCPLDKafn5+y9dRpKC4uVgb0f/7nfwDYGTOAPaLP6D6z3UOHDhVjg5nw/v37KxtA1sOCBQsUzTX4dbhw4YIyucx0JSUlqZ+YtV+0aNEVx/vVFjDqDzii9qtWrRLbhOy1Xr16YdasWTXzkP8BBgwYoHnJvurbty/GjRsHwMGOWLdunbSdahu2bNkiO8Cs0+7du2WjmK1t1KiRNAEqC2ZMWRNdr1491UUzM+fq6upUYc/k5GSEhIQAcIgz9urVS99JFmFWVpYYCmSgMLu5ZcsWZQGpYfLFF1+IlcHsakFBgeb7o48+CsDOTCIr0hnw9/dXNo9Hp+bl5SkTSBHMVq1aSX+N2T22t3Xr1vqZGeI9e/boZ/4bGxur9Ylsld/CbrVs2VLHfpOVEBkZqTWN7zwhIUH6SdR64Bo2YsQI9QN1W1xcXDQ2OVb9/Pyu0rIZP3685oYzkJWVpe/iM0ZERGiOkHXi6+srbSCyuyjq37ZtW/k5ffvatd9Onz4tfQjef8uWLcqmMsu9fPlypx1pDNiztVwfyAa22WzKaFfsA7J+yUqjGHL79u2VweXYmz17Nl5//XUAjix+dna2tFnIZOnUqZPTxckfeughMXU45ioKoNLeBAYGSpOGgqnMWkdFRYlpy6z1hAkTNPbo+33xxRfKcNNODxw4UFoozsChQ4fkS9KGN23aVGs8GW0hISFiQnGuMmvv7u4udhdFvPPz8+Ubsc+9vb2ls0PtjIMHD4pB4Ax06dJFNpAstri4OLWVrJIGDRqIwcMDODhneQ3gsK0VxwAZJ7m5ufLt+T5Onjypo76dhTvuuEMMUAqnL1++XM/NcZmQkKD9E20QGWBeXl5iotImL1iwQGODYz8lJUXrJ23QmTNnNNedgdOnT0t4vOLaRgYkxXqTk5O1PrLvqJ+zfPlysWPJXPTw8JAODe3XoEGDxLC58847Adj7nyy4ysDFxQW+vr6YNGmStCmpVVRcXCzGKlk+WVlZqmb44IMPANgZNFz/+d2RkZHyU7g+8Bm//fZb+edLly4FYNfzIqOJjNi1a9fqXfKQmMmTJ4sVV5MwwZlaiO7du2txv9ZQUlKCtLQ0+Pv7a6KTnpudnS2nkhNnyZIleOKJJwA4gi0xMTFyHOlYde7cWeJXpJhyYbvpppu0IeHi2aJFCxkrbrzOnj2rMgMK17355pt4/PHHq/kt/HehsLBQDmJFKiEXPlKBGzduXCuDM+Xl5aLEciFbunSpNrxclLmg1Tbk5+fLOeG/n332mQJS3LD36dNHG4Lahq5du+Lrr78G4CipvP/++3XaFAXwWIJQFZCuXnHjS/ozBSSDg4PlGDkDZ8+e1ck9PI0jOTlZpQUs7Wjfvr0EfunQcNNAJwpwOECJiYlXBQJ69Oihky8YwGvYsKHTTiQB7BsZBvkpSr1lyxbZE9KQCwoKdCIE20IBzLS0NLWBpVy5ubnaNFL089KlS5oHDFb6+vo6/dSjrVu3aozw+202mzZ/FOsuLy+XCDk3BkuWLAFg3wBT5JJUfU9PT4kZswRz/fr1Es3kvZKSkrRhcwYOHz6sgBnnTFRUlJ6JwTF3d3fNQwbJGKDq3bu3Nh/cNHXv3l3OPgMyNptNyR4G7p588klt9p2BJk2ayCfhOPP09JRvQkFSV1dXBWC4qeX1bm5usre85uWXX1bQnyLAR44cUb+xNGbJkiVVPimmqliwYIGSLty8pqamKmDDZJq3t7fmE+0Sx2DFMc0SmL///e8KAjMoMHLkSPlu3MDt2bNHASpnoE+fPgoEcR4dPXpUZWV87sLCQj07/VsGprKzsxUoZ1Bn7NixCsDxXj/88IOCAWxnaGioUxMgR44cka3k/OnYsSNeeuklAI5gRmJiogTxuWllYHPZsmWyReyXNWvWqF9ob318fK4qN9y3b98V4sHOwLZt27Rp58Z74cKFCkZwXp04cUJtnDt3LgBHmdaCBQtkj1jC1aVLF/m5nKNz5syRYDLHxv79+yXK7wyEhobqJD76aMePH1dSgoHPHj16aE5xbNI+dOvWTes677F161b5MLSjubm5CqhUnLNsf2Vgs9lQWFiI5cuXK9BDWxAcHKz7cs5kZ2crUM+EWmJiotZ/nuT7ySefKPjEw184zsLDw2UX+bu4uDj5NQyG+vr6Sgya4/LChQtOLS2sLExZk4GBgYGBgYGBgYGBgYGBgUENwjBnaiF69OhxlbDd8ePHlSWtSeTn5yvyz4go6YS7du3SMzLTEhgYqOwfWS82m02Ry4o0PGalGPFnFPb5559XVJyZtgsXLkgsioiKihJNmgyPYcOGKTJM5o5B1XDp0iVlBbt27QrAnv1iOQyzDa6urtfEGK0qoqKilHEg6yAmJkaRfGYLW7dufdXx8bUBvXr1ElON4ob+/v6i5qakpACwZ9trY/8B9uwyM4ZkB3z99deiuTIT2LFjxyq3kQwEsh1SU1OVGaIQ36lTp8QQcAZWr16tUhAyYQICAsTuIlsoPz9fY5THapLxFRsbqywas8dNmjS5Krs4c+ZMHYVLW9+2bVvRxZ2B9PR0lRG88sorAOzUe64dfN74+Hg88sgjABz0erI0PD09lTVlidb1118vFg1tV35+PlasWAEAEmfduHGj08VWL1y4ILo87Ux0dLSej21t1qyZsoHMNrJ/GzZsqDWTpTAdOnTQmslMdseOHSW2yLEaFhYmO+AMJCYmSnySTIFVq1YpC8tMdYMGDfCHP/wBgCPTy4zu+++/r1IRZjr3798vlizbl5WVpfnGLOz8+fM1N5yBoKAg2RaW3lQsQSALuEOHDnj66acBQGVILO/Zu3evmCYs3yorK1MGnKUzderUUdkT2Rf79++XuKazEBcXJ9+JDImLFy+qbIXZ+PT0dNnUikwFtoF+GplvXbt2lb/A0vOsrCxluDkHxo0bJyaUM0Tpe/ToId+TdqNHjx4aS7RxixYt0vrIucox2KNHD/m39Gnd3d3FJObvmjRpgrFjxwIA7r33XgD2ccD+dIaweoMGDVT6x35ZvXr1FaUwwJVsWrKFWZ4WFBQkW0gfv2vXrmIg8vqvvvpKPjvn7KBBgzSXnYUuXbqosoB7DxcXF81JllrdeuutWgP5/skg6t69u/w6MoLc3NzETqEt7tSpk+5LhlHz5s2dyu6qU6eOGKucAyUlJZp7LFc+cuSIbA77huvG0qVLrxLb7tu3r9Y9zsVRo0bpHbHUJywsTGOnMnB1dYW/vz+Cg4Ple9BOFRYWirlCmx8dHa3vIgu4WbNmGqP0Odzd3WWDuP7R5rRq1UpHf3OdSEtLk92l7Vy/fr3mNNea48ePa82oSRjmjIGBgYGBgYGBgYGBgYGBgUENwjBnajmYeejTp4+i2DUJDw8PREZGYsqUKRJupNhoYmKiajqZWRg2bNhVUcqkpCSJwjEafPfddysiz98xg9qsWTNFi9955x0Adg0JZsAZZa5fv76+n5mQzZs3XyGqbFB1BAQESMOHGYOcnByNxyFDhgCwMzCYeahN6N27N6ZOnQrAkaE/fPiw6lwr6gfUxvY999xz6j/2VVFRkUT1mLWNi4vDsmXLauYh/0OEhYXJVrKGfMiQIdJFYHYoKyuryn34Uy2BsLAwMVCYiVy/fr2y+s7AuHHjlKGnAJ63t7cyUGxT8+bNlX3nZxQs7dy5s+YvM1AtW7ZUJp9aLoBDLJM2OSMjo0p16FVFXFyc5h5FEL28vPR+qUfWrVs3PROZkMweTpkyRRl4igsmJiYqy8r3FhISomwa50B6eroyd87Cddddp/dPpsulS5fUHr7znJwcaT2Q9UKNg5KSEmWm+U7ef/993ZesgN27d2t9pA7MBx98oPXRGZgwYYL6gjazrKxM7DL2b1BQkLQ6KMDJuTNq1CgxMdmXHTp0EIuBmgL9+vWT7gT/LSwsrFLGt6oICQnRnCL74/jx42Iq8D1v375dbF1mjcm6aN68uXSOOAdzcnKuOkhhyZIl6j9q63h5eYkdOG/ePKe08dixYxpr9L+eeuopaXYxW33nnXeqbXwmfnbw4EFpcpGdkZWVpffDcd6oUSMdh871adeuXU7Nai9YsEDjkAy03bt3i5VIxkRoaKhYGfRluZb07NlTrDT6sUuWLNGcJuO2U6dOmDZtGoArD/ZwpnZXbm6u2HEVWVb8mYLWb7zxhtYJro38t23btmLuU7B1yJAhYjYOHDgQgL1NtFMUjn/sscfExHUW1q5dK/Yk1yRvb2/NIbK8d+7cic8//xyAQwOTx6d/9913Yjixb/Lz87XecK339vaWNhH3FPn5+VdVNlQnPD09pWXH7z527JiEpKk5M3XqVOmu0Ochy2fQoEESpub8PH36tJiN9JVWrFih+c5ju/Py8qTxVdnnbdasGRITE2XbOEZOnDiheU+facWKFVrz+J1+fn6a99xXeHp6as1+/vnnAUCaolFRUWIGcb1ISkoSY5PvaODAgXpv1JVctmwZHn74YT1LTcEEZ2ohpk+fjunTp9f0Y/xb2Gw2nTHPCUgBx+DgYAlTcQFesmSJxO5Il3VzcxN1lbTPmTNnSmGbxpUOdsOGDfW3pO/FxMRIvJT09lmzZsm54QLRpUsXc1rTf4jTp0+rXIzOVkBAgBYr0rOdKcboTKSlpSk4Q8emTZs2cvRZWsBNY21DXFycNj9c3Hr16iVnhsKPziwJcDaysrK0MaSg5ZQpU7QpovDcr6HKs5SBDpuLi4scCTrpJSUlTi9rYj/Rxnl6eqpMjZshNzc3bT4oxE7B0qSkJNGDieuvv173oEhrcHCwnE/S26dNmyaH2BkoKiqSwDw37Pv379eGZ9KkSQDspUF0wFgqQqxfv159xGBVhw4dJKbL8q3FixdLeJWlBe3bt3f6+D9//rzKAFiS27RpU4l0cm0LCwu7ytkmpX716tXqf9qnnj176sQVlnNZliV7xesiIiKcmuDZvXu3NnAMmLRr104+AZ3kI0eOKDDBecSNb3FxsajpdKDXr1+v8hf6CB9++KGcfZ4C1KpVK50CwhK96sTBgwfx1FNPAYBOlszJydHmjsHNefPmKWHEgOptt90GwD5mGQDgeMvIyEB4eDgA6P3Vr19f/cd3xbIwZ2LkyJEaQ3z2d955R6U9tAFHjhzR87FEgiUIHh4esrPc8JeWlmL8+PEAoMDVxIkT1TaWIfDdOAvu7u7yWxnkbNKkieYI29S4cWP5PNwY0mf94IMPFNihz7Nr1y7NX5b4lJaWau1gWZiPj498XmcgLi5OwR8GNyuWxPBUn9tuu00be5aF8Bk7deqkzTvbuXr1aglUc+zffffdClzRf8rIyNC8dBZycnIU8OVaERsbq0A2T3E9ePCgDgOhOD7tTHl5uYJTbPd1112nObl//34A9s093yPXm2XLlilJ7Ax4e3vrxCuKEYeFhSnox73V7t27r0ieAA5baLPZtO4z+Zaenq4xTDs6YMAAfc7x4O/vj+eeew6AI+D6c8jJycHXX3+NkJAQnbDHgHWDBg3kezIQGx0dLb+C/tn+/fvlS3HeTZgwQcEplktxvVizZo2CPvTPy8rK5OdRBiM/P18Bdb6Pfv36qe9rEqasycDAwMDAwMDAwMDAwMDAwKAGYTGyaPDbwLKs8wCSa/o5fiUa22y2kJ+7wLTvmsYvtg/4/bfRtO+ahhmjMO27xmHGKEz7rnGYMQrTvmscZozCtO8aR6XGqDNggjMGBgYGBgYGBgYGBgYGBgYGNQhT1mRgYGBgYGBgYGBgYGBgYGBQgzDBGQMDAwMDAwMDAwMDAwMDA4MahAnOGBgYGBgYGBgYGBgYGBgYGNQgTHDGwMDAwMDAwMDAwMDAwMDAoAZhgjMGBgYGBgYGBgYGBgYGBgYGNQgTnDEwMDAwMDAwMDAwMDAwMDCoQZjgjIGBgYGBgYGBgYGBgYGBgUENwq2mH+C/DZZl2Wr6Gf4DXLDZbCE/d8HvvX1uXnVsHn5Blb5hmV95pa91KaharNS1qEqXIy879RfbB/z++9C075qGGaMw7fspGjVqhMuXLwMA3NzceA/9LijIbpMzMzNRWFhY9SeuGpwyRps0aaJn9/DwAAD4+voiJycHAFBebl9L6tSpg6NHj1btiauOau/DsLAwtauoyL542Ww22Gw2/QwA7u7uSE9Pr/oTVw3V3r4WLVrg7NmzAICAgAAAQG5uLq677joAjvYVFxcjLy8PAODi4nLF9UVFRfD09AQA9bu3tzfOnDlTlUcBKjlG/fz8bMHBwSgoKND3Xrx4EYC9vzgeL126BODKeci+zM/Ph6ur61Vt5Od16tQBAJw/f15/V1BQAAD6Ti8vLyQnJ1d7G728vGx+fn7w8/PTc/N5PDw8cOHCBQCO93/p0iX4+voCgPotKytLc4//lpWV6W/YLtogV1dXFBcXA4Da6ePjc5WtKi8vR25u7hX3LS8vh5eXFwDgzJkzv9i+wMBAW0REBDIyMuDv7w8AyM7OBgD4+fnpvu7u7gDsfcW5x+vLysr0vOzv4OBg2Vm2gX/n5+ence7t7a3n5nW8r7u7O0pKSvROAKCkpETv/9SpU5UeoyEhIcjKykJIiP1yzh8vLy+1l+/N3d1dY9iyrCs+Axzrh81mQ1lZGQB7/wCOOefn54fS0lK1jdfz/YSGhgKwzxV+R7169QDYxxCvS09P/8U21qlTxxYUFAQ3N7cr2sXv5Lvj87i4uOhnfrdlWerDRo0aAQDOnDmj33H+sm/8/Pw09vlOi4qK9F28PjAwUP3Oe7m7u+vnzMzMX2yft7e3zc/PD5ZlaRxyrAQHB+s52Ga+b8DRL4WFhZqXvIdlWbIZHFMVbSftD39Xsb9pdyv+ruL38uesrKxKjVFnwARnDKqCKq+etQy/2D4PvyC0GP1opW94qVdBpa/1jPep9LUA4H+irErX7/jsyd97/wFmjNZ2/N7bB/z+21jt7Xv66aexefNmAI7NjYeHBzZs2AAAGDt2LADgX//6Fw4ePFjdX/9TOKX/pk2bhuPHjwNwONjdunXD119/DQDa3HXu3BkDBgxwxiNURLW3cezYsWoXneqCggI5wtwEhIeHY/r06dX99T9Ftbdv9uzZeOmllwAAI0eOBAB899136ituqE6cOIFdu3YBcGw+RowYAQA4duwYmjVrBgBYsWIFAKBt27Z49tlnq/o4lWpfcHAwpk6dioSEBETNMbbtAAAgAElEQVRHRwMAvvjiCwDAo48+ikOHDgEA1q9fDwCYOXOmfm7SpAkAYO/evfDz8wMAbXROnDiBpk2bAgA6duyov+XfJSQkAAAaN24MAGjVqhUmTpxY7W308/PDqFGj0LNnT2zcuBGAff4AQIMGDfDxxx8DcPTXN998g65duwIA+vfvDwCYP3++xibn4MWLFzFq1CgAwPvvvw8AuP322wHYN7SnT58GALWzXbt2sl+8rqCgAFu3bgVgD5oA9k1x69atAQDPPPPML7YvIiICixcvxhtvvIFbbrkFALBkyRIAQL9+/RT8CQsLAwDs378fSUlJAKDrL126pPnIoO/dd9+tTfuBAweu+Kxv374a523atNF74XW8b8OGDRXE4cY6IyMDN954IwDgj3/8Y6XGaEhICF5++WUsXLgQkyZNAgBs374dABATE4NFixYBAGJjY3X9V199BcCx4Y+KilLgkIGVoqIiBSHatWsHAFi1ahUAoGfPnsjKygLgCFgVFxfjyJEjAIDJkycDAL788ksFqJ566ikAwNq1a/WOX3nllV9sY1BQEB577DEEBQVh586dAOzzAbDbDM4tBqE8PT2RmZkJwBFo8vDwQEpKCgDHPHv22WeRmpqqZwKAwYMHAwD69OmjsX/vvfcCAE6ePKl+4hwfNWoUTpw4AQC6f7169ZCWlgYA+Pjjjys1B8eMGQN3d3e9e86L8ePH6zmioqIAXLkm3HDDDQCAxMREdOvWDQBQv359APYgDfuBNoY25+uvv9Zaw99V7G++06KiIgXk2M+lpaWajwsWLKgxX80EZwwMDAwMDAyuWfj4+OCHH34AADno586d00aKDmSjRo1+i+CMU3DgwAEFYuiopqSkaDMTHh4OANi9e3fNPOB/iMzMTGUz6QinpaWhU6dOAOyBAgC/BSvIKVi8eDFatGgBwJEZbtiwodgD3HgVFBRg+PDhABzsDG7cw8PDtbEcOHAgAHvAxlkoLCzEkSNHEBgYqA1Rjx49ANg30tyscuzNmTNHG5yKbDVm8L///nsA9s0SNz3cPBN+fn64/vrrAUBBKm6kqhvXXXcd+vXrh5SUFAwbNuyK7zx+/Lja+s033wCwB0P5Mze+e/bs0WaNgYdBgwZh3bp1ABwb+8OHDwOwb/h4PT/bvHkz+vTpA8DBTDp27Bji4+MBOAJBANC8efNKt+/SpUvYsGEDzp49i6VLlwKA3u358+fFKMjIyND7YCCQz5GTk3PVWDt16hQ+++wzAI7xwA358ePH0bt3bwCO4MyhQ4e0aWYQobi4GA0bNgTg2IwnJCSgV69elW4fYB9f//rXvzBmzBi1g99x5swZMZgY2I6NjcXDDz8MwBHEadasmeYVAxadO3dWgJGsFwZkDh06pKALERQUpAAQA33R0dEKAL344osA7O+adq4yuHjxIpYtW4bBgwcrKMMgYGRkpNYztj08PFzzjf1bWFiImJgYAMBzzz0HwB6YYoCN74MB4rS0NAWTeI27u7vWUY7Bxo0bK2DDwMrBgwfFsKkM3NzcEBgYiNjYWM2tnj17AgBWr16tNlRkaNEubtq0CYCd6VIx6AQAn3zyifqDtrVBgwZq3zvvvAPAYX+Sk5M1XhkA//TTT/VeR48eDQCIj4/XOKhJmOCMgYGBgYGBwTWLixcvaqNDh3/Pnj1o3749AEdWjxv82ogdO3bIQWRZzw8//CDnePXq1QAcDmhtQ926dbWpImPk1KlTygIPGTIEgIPZUNsQHx+vDR2ZCMXFxdpIDR06FIB9w8GNLjcmpPY3b95cLA5uJrmZdgby8/Oxd+9exMbGKjizf/9+APY+6t69u64D7Bs0Bme4cerRo4fmHfu3cePG2vRwQ8ggVU5OjkqBuDlOTEx0SvtycnKwYsUKhISEaJzxXR89elQBG9qR8+fPo0uXLgAcpTOWZcnmsJxj48aNel+0S9wg7tmzR9eTcePj44M9e/YAcIyDY8eOadNMpkVpaSn+9a9/Vbp9BQUFSEhIQNeuXcW4YUAtJiZGwQmyknbt2qWxSRsTFRWlQNOdd94JwB6k4NjkRpX337dvn5gHDAj17t1bG3ZumNeuXSsGEYORgYGBYtNUFnXr1sWECROwY8cOjTlu7h9//HG88MILAOzBesBeosYxzPl4/fXXY+/evQCAe+65B4A9APL2228DAF5++WW1A7CPGwZ9yLzIzc1V0IUBnjp16iiQwGDlSy+9hIULFwK4OjD57xAUFITbbrsN8fHxGi8MDDZs2BAREREAHIHckJAQMa4qlmVxPDJImJeXp3HNwMfJkycB2Mc75xzZa+vWrVNSgIy5jh07at3hfGjSpAmefvppAMCsWbN+sX3l5eXIz89Hfn4+OnToAMARaLIsS2sBg0TJycn6nAG3Xr16qf3Lly8HYJ+LZK+tXLkSgGMMzJw5Ew899BAAx9hPS0vTGP3uu+8A2N895zQ/GzVqlBIEa9as+cX2OQtGENjAwMDAwMDAwMDAwMDAwMCgBmGYMwY1Cjc3N0V8GS2uKDhF4SZGlA0MDAwM/rtw4cIFZUuZrWvVqpWyuqTjjx07FnPnzq2Zh/wP0b9/f2lEMEsYFRWFuLg4AJAmCLPRtQ0zZsxQJpcU/HvvvVdZTDIyWA5T29C6dWtl3sk62LBhgzLTZDScOnVK7CD6Pnwf4eHhYk5Qb4FsFGfAz88Pffr0QWJiIlq2bAnAUWJUr149lWlNnToVAHDXXXeJFcJ5tnDhQrEqWKK2du1asTYef/xxAA6WzOHDh8WmYXkB/1/dKC0tRXZ2NurWrassPEu0GjRooPdPVsXYsWPFaiJTwM3NTUwR+qN16tSRxsr8+fMBQIybIUOGaM7Sb61bty6eeOIJAA42R79+/a4opwKAbdu2abxUBpZlwcXFBU2aNBGLhX3g6uoqnRWWhH711VfqK7KifHx88OGHHwKwj00AGDBggEpbWEbZt29fAEDLli3FLCArLCYmRm256aabANiZgNS64T2Cg4OrLPxcXl6OvLw8HD9+XCWtLCsaP3683hdtZ3Z2trRhyIYg0wQA/vGPfwCws9TIrqANIqtn0KBBEp7ls2/btk1tY4lPcnKyfub1d911l0rLKgsXFxccPXoUY8aMAeBg5mzatElMOpYrderUSWwtrglJSUlim3DvFBcXp9K0V155BYBjPJaUlKhvON7j4uI0N9imbdu2ialJdmpkZCSef/75SrfNZrOhtLQUJSUlsh2cAw0bNsSCBQsAQOytRo0a6T1TwNjT01PsPfZz8+bN8cwzzwCAbCYZQvPnz8dtt90GAHj33XcB2O3Qli1bAEAsth49eqivWCK9cuVKlejVJAxzxsDAwMDAwMDAwMDAwMDAwKAGYZgzBr8pGF1mrWRAQIAyFIzqssYewBVHLjK7wmg4sx4uLi6KFjPjmJeXd9URdBWPSjMwMDAwqB3w9vZWTT91EdLS0iQuyQwbT2+qjUhLS1MWj2KdSUlJyn5TB4Cna9Q2vPfee1qLeRrIddddJ30GCj4yq1nb0L9/f+k9kAV04sQJZfap8dGzZ0/pk1D3gRlwX19fsS6oV0HGhTPA423r1q0rPQ/qTkRGRooNQf2Nbdu2iYXB61q1aoW6desCcIjtxsbGigH109OnUlJS8MADDwBwaJZQALW6ERAQgOHDh2PevHliNVUUD2d/UQPns88+k44FfdWLFy8qM3/fffcBsPdbZGQkAIdWBtkXt956q5gd/F3Dhg3FVCFOnz4t5glZOFlZWRo7lYG7uzvCw8Ph7+8vhsUnn3wCALjxxhvFfCIrfcaMGZg3bx4Ax/gaOXKkGCk8sj0+Pl6MCbIoOFYbNWokVg3ZGo0aNRLD6+9//zsAO/OLY56fFRYWShh5xowZlWqjZVlwdXVFXl6eNFPINNuxY4d+R+2SvXv3SqeFDJOtW7dqjpEdsmTJEo0JsjLZ5+vWrdPYIKupZcuWYpmQqXnXXXfpHZDR2Lx5c82NyuDy5cvYsGEDTpw4IcYd2SH+/v5X6am1b99ebeaamJWVJd0VauqEh4fr/VM7hX3p4eGhz7jW3HjjjRo71EXKzc3VPoo2ICEhAW3btq10+7y8vNCqVSvk5+dj27ZtABzsyLNnz4oh+Oij9lNwZ8yYoffAvWBpaanmChk/KSkpuPvuuwHYxwHfJWBfS8k++sMf/gDAvpbyvtShWb9+vVg8b775JgBgy5YtOv2xJmGCMwa/Gdzc3DQ5uJj7+/srsELaaVZWloS3uKgwmAPgKqXynJwcBAYGAnAI17m4uEhFnQEeE5wxMDAwqH0IDQ1VcIKOaf369fHpp5/qc8DhjNdG/O1vf8O0adMAOJzXtLQ0CVJSTJEObm3DihUrtGmhAOr8+fO1LpNKzg1QbYOPj482atzk7du3T+U8FY+9ZakPA28M1rz33nsq/XryyScBOOj+zoCHhwfq16+PxMREnQrGkpV9+/bp+dhvP/zwg07v4Tg8cuSIggucm926dVM/8p1wE7Zr1y787W9/u+I5+N3VDS8vL0RHR2PUqFHauN1xxx0A7IFcHjvNoEuXLl20GWUJxKpVq7Th5YY2KytLP/NfinmnpKTovfHvsrOzdRLPnDlzANhLiBhQ4AbRz89P77IyCAgIwNChQ7F69WqVXjFQnZmZqdNpWLa1b98+BQsrHu3Osh8GMKKjo68KXPXr1w+AfdO/ePFiAI6StaSkJAkBM9naqlUrndJE3/7ixYtVKokB7LZw1apViIuL06aZAaaHHnpIz86A386dO9U2Bhz79OkjIVkKQpeUlGDZsmUAHJt6buh79OihPQf/7dq1q05OYsnMjBkzFLTge922bZv2HpVBeXk5CgsLcf/99ysAVvG0PgaTGNyLjIzUfoeBvC+++EKlS5xvW7Zske1hySLbeebMGQVSGfxds2aNkgNsU+/evRX04XOcPHlSp7JVBm5ubggJCcHp06e1Z6PI+YoVK9QWzovWrVtrH8e1ISUlBQMGDADgEEp//fXXJQbN62lje/TooUATg2s9e/bU35II4OfnJ7vEwOT48eOviRMRTVmTgYGBgYGBgYGBgYGBgYGBQQ3CMGcMnAaKpzGaWV5eLgoosw2kKwJ2ES7AHr1mFJMMmpKSEkWJSe9jlsayLNSrVw+AI+OYnp6u6Dkj325ubvqZmQ0DAwMDg2sb+fn5WguYlezUqZMyjcxkvvfeezXzgNWAN954Q0fOvvHGGwDs2W1meplRd6ZArDPx1FNP4aOPPgJgP6IVsGeBSavn2k1mbG1DUVGRhCyZme3SpYuYBzy2edeuXfJv6CPRH5kxY4YYJ2vXrgUAtGnTRkyF6kZeXh527dqF06dP69lZGti+fXuJ/vLI4X/84x9invBo7KKiIh1TzXKXgoICsdk4bnnMbYsWLVS6xT6nIG11o6CgAAcOHEBmZqbGGW1GZGSk2CBkybi7u0u4meWTAQEBKlmhT1lcXCzhZL4Plo7k5eWp3OTrr78GAIwZM0bf0b9/fwDA999/r7FBH7ikpET+bWWQl5eH77//Hvv371f5GH1cDw8P2Q6y0iZOnCimCe1onTp1xPSiLICnp6cYUvv27QMAMRdGjBghlhCZGAkJCWJckamwe/dufPHFFwAcLLDdu3dXiXUB2OeIn58fYmJi8OKLLwJwsHksy8K4ceMAOMpiWG4GOEp2jh49KrtJG+vv7y/xZJZ4UdQ5JSVFc5iMkW+//VYMlNdeew0AcPfdd+sdsN9OnDghtmNl4Ovri06dOiE1NVXfRaH7oKAgzS0+93vvvad+ZdVB37599TMZbX369BF7hP3PuRgTE4NbbrkFAPDPf/4TgH0e0w6xbGvcuHGyC2TQ9OzZU9/PcqKfQ35+Pnbv3o3CwkKNIc6nunXral5wTEVHR+u7+B66du0q+8E95NSpUzVGOaZZ7paXl6fqjMLCQr0XMsN4r4yMDNk4lrZZlqXSx5qEYc4YGBgYGBgYGBgYGBgYGBgY1CAMc+YaBUWMJk6cqCwLI4Bz584V84S1nNcimOlktqFu3bpivzArERMTg9WrVwNwiATGxMRI9IlH2+Xn5yuqzawLI77NmjVTBorR4GXLlim6Xp3HcFuBpXAfcb7S17/UbH2lr729V9Uyok2+qqIw5GdVu/yX8GvGKEW3GJln34wbN051xKyZvXz5slPr7X8JbF+zZs0U3R8/fjwAe7aD2QO2vaCgQOOQOkesJW/cuLHqy5kNyM7OxquvvvpbNOXfgu1r2bKlshE8SrNZs2aq4yVDLTg4WHXVrGVmxsLb21tZN2ZfDhw4IJHPmgLb6OXlpedjtmfYsGGyR8xchoWFKdvGzC+zwWvXrtVxi99++y0Au71h5rQ6ERYWhnHjxuHs2bOaIxQJfOKJJ1QvTvvYs2dP2U/a3bCwMGWqmNWtX7++9C14X/ZXly5dlOlkHXaLFi2cIo4XGBiIfv36wd3dXQKRX375JQD7OKMdeeSRRwDY7QnZFtRPuHz5srKwtBnl5eUSpORcJBNhy5Yt0pag1kTTpk2dNgcbN26MqVOnorCwUO+cY6miBgWffdCgQepXCh1mZ2crc8rxe+zYMfzv//4vAEeWmPoQGzduxLPPPgvAkdWOi4vDY489BsAhsJienn6VFkpxcbHWT7I8fg7XXXcdunTpcoUoJtkEu3fv1rPx/pMmTZIIKNfmhQsX6juZed+/f7+OV6VYMI+ePnToEDw9PQE42Dc2mw2zZ8++4v3269dPRxxTw2XlypWy39TK+DnUr18fEyZMQEJCgjQgOGfGjh2rNYFMhZkzZ0rziCKew4YN09HBPML38ccfV6ae11OvIzMzE/Hx8QDsYqOAnU1DRhjn6ueff47bb78dgEOEd/jw4dJKoHbRL8Fms6GsrAwtW7bU/GOWfdu2bWIW8Dvq168vm0k9k/LycjGXycAICAgQM4OaEmSRDBo0SFoSXAvbtGkjW8U109XV9arrIiIilPV+//33f7F9JSUlOHfuHKKjo7W2USx21apVWs84x8rKyqTtQb9l3LhxEjNm//fo0UNivxyPFRkUZLFQkPerr77C/v37AQCTJ08GYB+/9Hk4Vxo0aCB/guyBX4KrqysiIiK0PnCM+Pj4XDXH161bp2ejj71z505p45B9UVZWpuOoOX7feecdAECvXr3kg/OzCxcu6JhkPseRI0cwcOBAAA7Gxp///GfZKvbHL6FOnTro2LEj/Pz8NDbIMOnZs6dYV/wOPz8/jU0ys3JycvQOqKvy1ltviRXEtZ73jY6O1npK0eDs7GytLXwXWVlZ8gPp88XExFTpuHAvLy/ExsaiTZs2mlvUPSoqKtL74niLi4uT7eMz+vn5aY5w/CYnJ4ulxfHNZ12zZo1YQDxaPTw8XHaX9u7cuXNqF5lBbm5uVRKt9vLyQosWLdCwYUOtE/Rb3n//fbHyKmot0X6ymmLWrFnyUfk+oqKiZCupWcV9RUFBgWwr7UXdunU1HviO5s2bp5+p6RYSEiL9m5pErQ3OWJYVBuBNAJ0BZAMoBvCazWb74lfe7wUAuTab7X8ty5oGYLPNZqvyztCyrDgA4Tab7etf8xwEaXMVKXrEfffdp4WAAlVVRWpqqkTZqL5f3aCDzQXbxcVFdD+KOx44cEALCJ00V1dXOZHbt28HYHcKSLelw0BjcfbsWV1P4wM4FnmKW6WkpMhAk5Jr8Ovxa8bo5s2bATgWQxrTrVu3ymliH44YMUJUVGeN0Z8D2zdz5kw5+HTEFy5cqE0wF8rFixfjoYceAuBwhrjgWJYlB5s03JKSEi0INdm+TZs2KQDD587OzlbAgo5Ghw4dRBUlXZYlCI8++igefvhhAA76bcuWLWu0fYCjja+99pocfT7fjBkzdPLNlClTANg3t7Q5HI88qeP8+fOyORVP2XBGG11cXODt7Y127dopuMBg5/bt2yVcSKfr8uXL2mjQidm2bZvKY7j52759u5xntoUOeVJSkuYeNxKjRo1ySvtcXV3h7++Pvn37KnjP4PvFixdx8803A3AEPvfs2SMRWV43d+5cnaxCGrK7u7v6j2OVm5Fhw4bpd3Q+GzVq5LQxmp2djcWLF6N169YaL5xf7u7usg0U8Lzvvvs0Xun87tixQ446g4YuLi6yubNmzQLgKOtq3bo1XnrppSt+9+abbyqoSEHGdu3a6X6kk3/66acKmlQGISEhuO+++7Bjxw5tCLlRjYqK0vtk22+99darRH//8Ic/SKjzqaeeAmAflxwT3CByIzly5EitCXx/5eXlmtMcI5cuXZLTzY3AyJEj9XyVQUlJCc6ePYuYmBgFGLi52LVrl9rATdOIESNkM/n8y5cv11rH+bl27VptOhi047rSuHFj2SkGcG644QatpdxIXbp0SUktlh3s379fwbGqwLIsHDhwQHOJ5VT33HOPAqFsa3R0tNrI37Vu3Vq+GQOIFU9So03h5r19+/byDXka1JgxY/Q79uEjjzyiwCmDsI8++qg215UJzri6uqJOnTpISUlRPzChcO7cOb1X+oPFxcUKEvJdrl69WuObAbng4GD07dsXgEOMlM+fkZGh9ZGBe09PT5VUsKSjsLBQfgWD/cHBwVU6uKK0tBQZGRk4e/asxgH9+kceeURlHwy8ubq6yh9jGcfWrVv1HLQ7Xl5eahev40a4e/fuGhf0xdPT03UP/h0/Axzr1NatWxV4ruzJemVlZcjJyUFGRoZsIW3KDz/8oPWMAaPo6GgFExmsTUtL0z6Dge2JEycqCEAbzL3K4sWLNc6efvppAPagDk/54TioV6+ego4sudq8eXOVSzPLyspw4MABrXccW4sWLdI+hiconTlzRn40AyyWZalPKpYmMRjB8j36qv7+/upP7sWKi4t1He1XXl6e/A724ZkzZ7SmVgYeHh5o0qQJ1q9frz764x//CMA+/2mfuR7t3r1bY46Czs2bN9d7oG+Qm5urMUdbxIBq586dJfbMfWX9+vWvsmcdO3aU/abduXjxooJatIU1gVpZ1mTZR/6XsAdQmtpstg4AbgfQ4CfX/argk81me+7XBGZ+RByAW37l3xoYGBgYGBgYGBgYGBgYGPyXobYyZ/oAKLbZbFIRs9lsyQDetSzrjwBGAfAF4GpZ1mAAXwEIBOAOYKrNZvsKACzLegbAOADnAJwG8MOPv58DYIXNZltiWVYHAG/8eL8LAP5os9nOWpa1CcBOAL0BBACY8OP/pwHwtiyrO4DpNptt0a9pICO0bdu2lYAZxY7atWunrATpvqdPn1Zm4qcoLS1VRoyZK8BBHXN2VpuZgPT0dJUZvPvuuwDsQnOMzJJK5ubmpqwFI71LlixRJPvjjz8G4MgYDRo0SNl9Rlc/+eQTtZW0PBcXlyuO5Db4z/BrxigFIZmlI532/PnzyiI2aGCPsZ47d06Zp5pgXrB9Q4YMUTaC0fguXbqIEsssQ3R0ND77zF47RqYC0aBBA5V03X///QDsmZ5roX2dOnVSBoJZ6dmzZ2Pw4MEAoH/nz5+vOcdsFfv78ccf13zjHIuIiKjR9gGONj7xxBNiaTEbP27cOGWqKKJXWFgoJgGzKxMmTABgH79kPrDsZ8WKFU5rY1lZGaKiopTtZ9a+S5cuotnTPi5dulRZa5bLFBYWikLPz1atWqUML8t+SPtt0KCBGCUsSRg7dqxKQaqzfTabDcXFxdi0aZMYBXyuuLg4sV/IaomLi1MWkPPN3d1d7SKLLT09HbfeeisAB8uAfRsYGChmCsexp6en0/ovLCwMjz32GDZt2qQSFDJX3N3dZVOYta9fv77GY0WKPjPtzAAWFhaqTI0MGh4R2r9/f41v3mPgwIEat/zOzZs3qxSF3xkZGamxUxkUFhbi2LFjiIyM1L1IOd+3b5+EQVnOmZycjKlTpwJwjNHGjRtrDJP9lJWVJdvK0h6+t3Pnzikrzcxv165ddT/apaKiIo3rP/3pTwDsvkZV6PguLi7w9fVFfHy8WEhkVXh4eGgMcZylpaUpA8/Msqenp5i7HMvJyckqq2OZDG1Nxefme0tNTb2K5fbiiy+qlIuMkPfee0/2rrJwc3ND3bp1ER0dLdZAxTJOlnnw6NtNmzapj/m9r7/+ut4xj3MOCwtTyc706dMBOPzA7777Tv4o/cBZs2aphI2Mp1dffVX347j54IMPxH6rDFjW1KVLF5WAcIz069dPZSpkb23cuFHvmvZxyZIl8knatm0LwO5z7tmzB4DDLnMu3nfffcr8816cr4CjzMvDw0M/kwnZp08flXlUBjabDeXl5QgODpbvwfncrl07fPrppwAc775r166yJ/RlOnfurHfD9p08eVL2huxLlpgePXpUJUlkab7wwgvy7SqyGFmyyrG8c+dO2aLK4vz583j//fcxduxYzX2+X1dXV80lrskVWTzcRxw8eFBjmX0dGxurMU+wT+vUqaNxSFb+qlWrtBbzvs2bN1d7WF5VUlJSJWYJRZ2TkpLEAKQf1rdvX80VskhOnDghm8J1b8WKFSoLJAMlICBAz8u9E/t8586dKv3iZz4+Prqe/xYWFoppRTZJSUmJ3kllkJ+fj717915h12njHnzwQa3rZEWFhIRoLa4o6stnot1v1KjRFf4q4FgnTpw4of5mm11dXVWiRrsbHx8viQ36HA888IDYXTWJ2hqciQWw52c+bw/gepvNlvUje2akzWa7ZFlWXQA7LMta9uM1t8POdHH78X5XeGeWZbkDeBfAcJvNdt6yrNsAvALgTz9e4maz2TpalnULgOdtNls/y7KeA3CDzWZ7sPqaa2BgYGBgYGBgYGBgYGBg8HtFbQ3OXAHLsmYC6A677sxMAN/YbLYsfgzgr5Zl3QSgHEAEgDAAPQB8YbPZ8n+8x7J/c+sWAFoD+ObHiK0rgLMVPl/6478/AIisxiYpks1/AajGGHDoqDBCvGvXLmWgfoqCggLV0zHrERQU9JsdFwSdRNEAACAASURBVMbIckREhETOKJT28ccf6zkYGfX19VXWj9nUQYMGKbPCbAprBQ8cOKB7sMb26aef1vtixry8vLzWHtN5LeLXjFFqsjCqzYxOvXr1lNFiJrJnz54SUawJsF0jR46UBgeZBQcOHFDGgTX4oaGhyswyo8/M0urVq8VsYwZi5MiRWLqUJuS3B9v38MMPi8FE4dCQkBC1ldmZLl26qG6eGhCs405LS9N74HwrKSlRFq+mwDYOHDhQmRGymry9vZUJ//zzzwHY+5dMBvYv38P27duV3aHYaFxcnFPsqJubG+rVq4elS5eKeUZ2wqJFi8Rmon6Cq6urnpPMpZKSEs0fsprCwsKUSSLrifcqKSnR33IuXrx40Sntq1OnDjp37oy9e/dqDDGjedddd4ntyJrvhg0bSsuAzJkbbrhBWWAKoXbo0EF9zjWP2gdr1qyRXg1r00+ePHlV9rS6cPnyZWzcuBEnTpwQm4UaATk5ORp71IOpX7++soIVj6Xl35JdkZCQoHaQScFM8oIFC7QGcpxnZGSIMUOGSZ06daSbQI2K2NjYKglZenh4ICIiAt98843YT2QKPPDAA+pP6swlJiZK44NrwoEDB/Q3zFpfvHhR9oUZdzIzx4wZo+wrbay/v780syj0+8ADD0gkkvPe1dVVbICqsBNcXV01ZyZNmgTAbjuo48Fnbdu2rfwWPvfatWulxcU+dXNzk2jz66+/DsDh0wQHB8sGU5Pjn//8pzK+HB8XLlyQJhbHb8VjkisLm82GkpIShIWFSSid9r5Tp06aa7SPN9xwgzS4qBvRqlUrPReZ0b6+vrJb1F/h+ldcXCyfkILxy5Ytk5YE+2jDhg3KnJP9Fh4eLjYGmSI/BxcXF3h6emL9+vVai2n33NzcJFhLBk/jxo21FtJ2jhkzRjaSGjgHDhzQ++I4o7bOypUr9bycg82bN5dvwDYdPHhQc5pzfNq0aWI0VAYFBQXYv38/cnNzZUcoDFtRd4m6IhWPlKYtioiIkBAsNUe++eYbsXpoO6ipMnz4cLGEON6Lioo0RyjEWlhYKHtCvZLU1FSNkcoiICAAI0eORHFxsb6Xz/baa6/hiSeeuKI9Tz/9tPqErPz09HT1F9uRmZkp28A5TFvUtGlTvPXWW1c8R7169WRvOefWrl0r20u/Jzc3V/5tZeDm5obQ0FAMGzZM69jGjRsB2NdmslTYvl69euldU+9m5MiR8qVZEREQECAWKXV2iJtvvlnvkoycmJgY+Qncp+Xm5sofoi2IjIyskmAu18HmzZvriHkyZ9zd3dUvZCyvWLFCmjs8EKRZs2ay+/wsJydH+w2u8RyrW7duFTuGYyEjI+MqPcEbbrhB7aNfvmHDBrFp2B81gdoanDkIYDT/Y7PZHviRFcM3mVfh2rEAQgB0sNlsJZZlnQLgVcnvsQActNlsXf6Pz3kMUBl+43dJ6lpFUa2Km+SfggOfRi0hIUEG2dmgQUtOTlZwhMEZm82m39EhLy8vV3kSjfvevXtFt+VkoxGKj4+/6rN33nlHE5YLg6enpyh0Bs7HvxujpHbTiLJ/i4uL5ZTQYXnppZe0cNQkvvrqK1G4+bx9+/bVBo8OQPfu3WX0ucgzuBgTE6PgIz/LzMz8zebgz2Hbtm14/vnnATg2UpcvX5Y94QL/7bffysEkfZ+breHDh8spZEDm+PHj2sDUNNLT0xVs4aa9b9++6gs6tiUlJXLiScfnuGzZsqXaS5p/UlKSaNXViezsbCxZsgRTpkzRJpffuW/fPo1DBiyaNGkiO8e5FRYWptILBs4OHTqk+9BJp4Pj7e2tTSIdsdLSUqeMUXd3d9SrVw9ZWVkqyeHatGHDBm3eOMfc3NxEW+fvjhw5onWEwbJRo0apFIbt4/X16tWTLaLj1qBBA6fNwdLSUly4cAH79++X08vgXp8+fbQWcf27ePGigmIMsBw/flwbSG6a//znP6uvWW5Tka7N72L7v/zyS4lJMiDj6uqqUis6wM2bN1eArzInWLm6uiIgIAARERF6Rm4Wli9frrFJ0ddNmzZp88e2c/4BDpsSHh6ujQ7byc3QZ599ps17QkICAPvYoN2iHSspKVGbKbZqs9lUelEZ+Pr6olu3bujZs6eCHnxXpaWlKlXhyVapqakKZDIwERcXpw06A4mXLl1SUJ5BKvZ7Xl6enpGByXHjxsnucPz06tVL75qCmpcvX9Z9Kovi4mIkJyfDz89PgXfa8cuXL6vdHEuenp7afLMPR40apTWQ8zAyMlKbVq4HLIXp06eP+oQbTpvNpnFLexAcHKz1n0HjlJSUKoke22w22Gw25Ofna2NGUdKWLVvKX+RYstlsCphxvU5NTdVGlmuBr6+v+p3isww+sW0VUV5erndIoee//OUvOlWLc/HChQtVKr339fVF9+7dsW/fPgVeOQZXrlyp8hoGT1NTU/Uzy1WioqIUMGHgqGHDhpp79H34/JZlaT2p6M89+KC9WIA+QpMmTTR+WD52//33az9QlTZ27doVZWVlGmcMpgwfPlxzgwGFpk2bSiyafTRlyhQFGDm+CwsL1TZuzCk8nZCQoOsYBK5YXsj9SVRUlOYfN/LR0dH/p8TEv0NpaSkyMzOxe/dulTAx0Dd79mwlZSqe6MY+ZBlavXr1ZJs4j8+fPy8/gYFUjr3Q0FAlHFkO9cMPP8hmM2Bx6dIl9Sfn++LFi6tUWujt7Y02bdogLCxMpYIMJK1bt05BWdrTlJQU2Uf2T0FBgeYI7UlSUpLax/5j+WFKSor6hf7r6tWrtb7RvykuLlZSgOtwXl5elcpfnYVaKQgMYAMAL8uyJlX4nc//ca0/gHM/BmZ6A2j84+83AxhhWZa3ZVl+AIb+m789AiDEsqwugL3MybKs2H9zXUVcBuBX2YYYGBgYGBgYGBgYGBgYGBj8d6NWMmdsNpvNsqwRAN60LOspAOdhZ8s8DcD7J5fPA7DcsqwE2Jk1h3+8xx7LshYBiIddEHjXv/meYsuy/gDgHcuy/GF/X2/Bztz5v7ARwBTLsvbhPxAErk6EhIQokszI5bRp0xQJdzaYya3IkmFmzGazKUtKlkFpaal+ZobTZrNddXwsMwylpaWKcpOu/OijjyoDxc/Ky8urdEyhQfWDFEaW4LH0KTExES+88AIAx/HHq1ev/s3G6M/h3nvvFY2UmYqkpCRF9Zk92rlzp+iszLYww3D69Gkd48ds/9y5c6+J9jVt2lS0dGakvL29/z973x1YVZV9vW56b5BASOgl9Kp0pKmgghQpjiJNEVFnRsfK2LF9I3bFMhYEBEQUlVFQRFAUQYqAKUASSIAECCRAICGkvu+Py1rvAQ4mTsJL+J31j5i8JPfcs88+++y99jqqwnL9Tpo0SW2RrFiQaVFaWqqKBmns48ePrxbjA+xKIAWnWT1JTU1VJZqVqrKyMlXwWZ3iXH722Wd6P2yjuO6663SdcWUiLCwMw4YNw8svv6xWCrI+QkND9d4puvr999+rSsqK+5QpU1TVZeU3IiJCn6MvZNX40KFD8rH0u7Vq1aqSOczNzcU333yDVq1aSVCT73bBggUSIeZ6cm2bY/XSlXXBiueIESNUgecesnjxYgA2S4EVM/7sVVddVa4ref8MPD09ER4ejnHjxsm+WOXdsmWLRFTJGF27dq1aLsgUKSkpEdOJ1efDhw+r+nzfffcBcO6n3bp1k22QiTRq1Khzxr1//35VQsnySE1NrRBd/eTJk/j111/h6empijbnqGPHjqqasyWkrKxM1X2KjM6cOVPtyWwFue+++1QBJeuJrJMDBw7ob9CPFhUViW1JJkRQUJBsn/bdvn17sU3KAzKDli9frvXD6nu3bt3E4OG12LNmzRJblK1i33zzDT777DMATuHYIUOGqCWH7U9kXG7evFlfY/vOrl271NrAlt/ly5fr31yfbdu2VbW4vCDzYs+ePVovtL2jR4/K99NWf/31V8VTtDMPDw/5fj57u3btNN7ly5cDcFb+ExISNK8cQ0hIiHwO32dhYaH226efflq/tyLC3QUFBUhISEDnzp3FjmTMsW3bNvkcVs9ffPFFtTNxLh0Oh56D4wwMDFRLDxkoZBsMHTpUDAi2E7Vs2VL7P9luy5YtU7xKgfedO3dWiBnk4+OD+vXrIzIyUqw4rou2bdtqXfDdHz16VM/GMWVlZUk4mG2BI0eOVMxDJunUqVMB2PE31yfjuYYNG+pdusYGZK9TqPWrr77SGi0vTpw4gVWrViE0NFTtPmxxKSsrEyOWf8PLy0uMNPrbl156Sc9MG/36668lmE/2KD/fr18/vR/GPP/5z3/kK/m5gIAAxUm8cnvbtm0VYuh5enoiODgYX375pc5HbPW9++67xXah/YwaNeoMSQfAni+2LjHmbNu2rXwf/TNtw9vbW+uAzN+2bdvKV7EVs0mTJvoafUvXrl0VI1GI93woLS3FsWPH0L17d7HKyNAZM2aMfAzj6djYWK0LXv4yd+5cvRvOX1FRkeaDba+MAwBnGzjjptTUVDFS6UNCQkLESCXjyMfHp1ow2mtkcgYAHA7HAdiCvr+HD1w+lw3gd9uSHA7H07AFfs/++kSXf28FcNnvfKbfWX+j0el/HwFQMe9jYGBgYGBgYGBgYGBgYGDwfxY1NjljUH7ceeedyn6zysJs8IWEK3OFlTzAqUnDzKirYBw/b1mWKhOsZDMTnp2drWoOs6XLly+X6BSvhc3Pz1eW1sA9YIabjAX27mZnZ+vqbVaiXCvj7sQ333wju2IFvH79+udc49u9e3dVHTlOVlyPHTsmW2ZlrEuXLtITcSeio6O1BvncRUVFqoyymt6zZ0+MGTMGAFRpozBp06ZNpTfw5JNPAkCVaLH8WTRu3FgaAtTSiYyM1BySjde4cWNVkCmmyKpm8+bNZaOsSLFvu7Jx5MgRzJs3D6+88gr+/ve/A3BWr/39/dWTTc2GVq1ayaezMrl06VJV/1ip2rlzp6qptEfXa385n6wQr1mzpkrGFx4ejhEjRiAjI+Oc/vxx48ZJT4ysi1WrVqkyymp7aGio1hIZEe3atdOccL6px7Jz505dkclrYatSsDosLAxDhw7Fzp07NTcUM5w/f76Yn9Qq6N+/v1ghtNFx48apIkqW1JIlS1TlI9uA2kKJiYkSXSSbZs+ePdoLaRsff/yxrhBn5Tc5OVm6PuWBr6+v1gtZKmSneXl5KeagH8jJyZE9Ugti+/btmDBhAgCnkGhxcbEqwrfccgsApx7BwIED9W+yvG644Qb5o3/+858AbN0PVprJbNy6davYcOXB0aNHsWjRIgQEBIjpMWTIEAB25dVVtBmw4xIyXFhF37Vrl+ybWiArV65UxZeaXGQzNG7cWPoJZMBNnTpVvpV2kZ+fr+ruyJEjAdgVc8Y65UVBQQHi4+PRuHFj2Q0ZWtOnTxfLkPMbFBSkfZnjmThxoqrqZATt27dPrBH6GdrGhAkTZOfc/+rWrav4j3O/d+9eXQ3O99WiRQvZsuv11P8NwcHB6NOnD+rWrSu2Axkefn5+YtmRrbN06VLZlasQPOeCa2XdunXS1OEapJbNqVOnFNfQnx49elS2R8ZCSUmJWDrU44mJiZFPLw/y8/OxadMmREZGKranT6tbt65YLGQl/PLLL/r91CiZNGmS3i/HsmLFCs0vY3GyS9LS0nDHHXcAcF4znZOTg1dffRWAfaU7YMcQXJdk7Hfp0kXxXXlRu3ZtTJkyBfv27VOcwjjMw8NDDCe+/8aNG0vnivEMdU8AJ4tk4sSJ2ttp+3zOWrVqyW+Rwebh4SEdK7KIpk+frtiQth8YGCg/UR6mXl5eHn788Ufce++9mkNqaO3evVt+kftZcnKybIg+dv78+ZpjxqCffvqp7I9rlnp7fn5+YjVxfoODg8XS5dfef/99iVUz5khKSlLcVB6UlpYiNzcXhw8fln2R2bR06VLZI/fInj17il1GNmVsbKz8CPVgmjdvfsYV7YBzv/D39xeLhu8gJSVFsR116dauXat4gQxWf3//asH4tsxh9cLCsqwL9sJJ41y9erUWLDcUUiAriM0Oh+O80Vtlj8+V3s2FyMCUAXxISIicNp1QSkqKHK2r0PAf4A/HF+oT5ehZe0y5nz/l703K/dkxV/1U7s8CwLp//P7tXP8N3383/Q/HBxgbrUxcbONj4M5AmmssLS1Na5BBDdtyKgi32yiFgLk3MijatGnTOWKNrVu3VjteBeDWOWQAw4M4A6DQ0FAFKjy09O3bVwFbBeDW8TEBQqFB7gknTpxQsokHrzVr1mj8FYDbbZQJG46HQfdjjz2mQygPV61bt9YNNBVApc/hxo0b9dykl0+bNk0tTgy6IyMjtWdzvVG08uOPP9aBkIfo1NTUM0Tny4lKH9+XX34pW2LxISgoSEluHhYmTJigVgUmCpic2bVrlw4T/B1Hjx7VO6oAqsRG582bp/fOOOyLL77Q/PAwzMQg4BwjEyAffPCBEqw8WB86dAgffvhhRR4FqII5HDNmjJJjbJ0JDw/X3sY5iYqKUisUCxZslWjbtq0Od0wQRkRE4KmnnqrIowBVML4333xTa4vtZtOnT1cLIhMnQUFBSoaf3UbYrFkzJcj4rmbOnKn9hC2/eXl52kfoi7y9vdUOePXVV1eJjd5///0q5DKJ4SqfwGTp1q1b9Xz0KUzkurYJMwHywAMPqA2KSYaEhAQlUvg7kpOTlbx46KGH/nCMDRo0cNxzzz0YMGCACieuCWgmUZhYaNWqlRIlbIMNDw9XcoNF66ysLMkHMGHHotLWrVv1e5mYA5xJL7YfTZ8+XckZ/v5Dhw7hxhtvBABcfvnlfzi+8PBwR79+/TB06FCd2Rg/enp6yjaY0MzMzJSAMW9R27t3r5LgHF9iYqLael966SUAUPEsOTlZ88f3tnbtWs03BeyLiorOSSIXFhbqZ1599dVy2WhVoKYKAhsYGBgYGBgYGBgYGBgYGBhcFDBtTRcxSAn39vYWHZbXCdYUMMPq5+d3hrgq4MxUe3t7K+PNquHOnTvPEBM2qJ64GGz0fLjYxkcWAsUCSfn19PQUrZaUX1aBahpYdWNVl9XPG2+8UZUzthSQFl2TwNYSVoEpuuvp6YkbbrgBgLPazSp5TQKZB9wvSHFfsGABHn30UQBOu42Ojv4zzBm3g202ZCOwqpiYmCjGEKvgrAy6G19//bVYW3y22NhYCTZzHmrXrq1rcdlOQoHMTp06ncMMGjJkyJ9hzlQ6jh8/LhFmV7FtirGy9Wzp0qUSAGY7Bf//5MmTYhbce++9AJwtKdUBYWFhanHiWCdPniwWKGOzTp06SbSUbYVsi4mIiBDrhsyv8ePH/xnmTKWjYcOGmD9/PgBn60VcXJzaJhhz5uXlqZ2Z4s9sP83MzNT3yDqoLn508+bNmiO2AX322WdisnFtZWRk6PvcB8mS3blzpxhtvD7b4XCoNZgxj2VZ8j1kKiYlJenvVxXCwsJ0biBz5uTJk5oDMoG8vb0Vi5FRQZbsggULtBdShDgsLEx+i7a/e/duCV/z83v27BHrqDzIy8vD2rVr0aBBAzFcyOzIysrS+6KY+sGDB8UyIaMrICBAzCCyR1JTU7VPkM3E57rpppvEYmFL4i+//KJ1SV+1f/9+MYfYxjl79mytjfIgICAAXbp0QU5OjuyLPqR58+byj4ylhg8frvX24IMPArD3CQr3k/k1ZMgQ7e1kEPHn/P399Y54Drzjjjs0v/xbPXr0kK8mU6558+YVGl9VwTBnDAwMDAwMDAwMDAwMDAwMDNwIw5y5CEFROFbQioqKVDF0FeKtCWBmuEmTJsp8s+eQGeW4uDgJqvEzJSUlYswwi24YNNUHF5ON/h4u1vF9//33AOy+cwCqjpaUlEiYkvoX7F+uaWCvMnusqaewb98+6dG4+p6aBjIOaKNkmHz99deqQLGKTb2MmgQKKFJslHP01FNPSXyQ2kmscNc0kP3Eq2x59XhycrL0Clgtvffee2W37gYFcCmiOXnyZAkiU6fhyJEjEnom64I6VldffbXYCKz2U+DT3Th+/Lh8HxkWDodDsQmrxh07dlT1l/PH+YyLi8OsWbMA4Iyr1quLuPqaNWvEjKAfSUpKUkxGPY9Fixap0v///t//A+BkarRv315Ve85hVbMpyouIiAj5CLKxrrzySjEwKGS9ZMkSxZrUuKLOzIEDB6TnQmbbkCFDcPfdd1+gUfx3dOrUSQwY7tfx8fESrnUV4KaIPNkWXKfXXHONfAzZCY0bN1aczXW8Zs0arWNXvaU333yzCkdorzOyXTmXd9xxh65nJjulrKxMuiu0P+6J48ePF5OCWjqFhYUShKadN2vWTJozFFgOCAjQ3y8PAgIC0KlTJ3z//fdisfIdpqWlyc7oR7p06SJGEueoqKhItsbrp2NiYvT+yWoiiyQkJETsWDIWY2JidC05/dP27dt1lTbf5RNPPFGh2M7DwwNBQUHYt2+fWDh8Z6WlpfpdFOnftm2bbG727NkAbJ9BH0NmW0hIiGJpvi++v+joaGzbtg2Akwm9fft2DB8+HIBznsPDw7V/XHfddQDs90y7dicMc8bAwMDAwMDAwMDAwMDAwMDAjTDMmYsQ999/PwBnBfHrr7+ucToX1ENgH+WePXvUG8hrs9kfGRERoX5I9n+6smQMY6b64WKw0fPhYh0fq2O5ubkAztSO4PWx7L1v3769tFlqEliZYd81fVDDhg11Iwdvlrn11lvd8IT/G1gxYwWVVcMtW7ao4s8rifPy8lS9qilgFY03VZBB0rhxY90mwxsq+vXrhzlz5rjhKf83sNeezANWDDdv3qwqP2+RodaHu3HZZZeJicWqputtGazqDh8+XFd+P/PMMwCgiu6OHTt00w/ZJLyK291YsGCBmGecj4KCAukrXH755QBsJgJjGLL0aLMZGRnSmKEWBD9THeDl5aWbUVjdbtKkicb2wgsvAAAaNGggzTEyaFj5zs/PF2ONPqi6MPSCg4P1nLfddhsA2y550yJvyTlx4oRuXSQLgHMeFhYmP8pbqyZOnFgtmDNt27YVs4IskEmTJkkziEyuefPmiW1HhhR1WbZs2SKWELUe69evr32ELI3IyEgxbfmO6tSpI5bYF198USVjbNy4sWJ+/jcpKUnPxSvN8/LypKFHRgdvn5o4caK0UHhj0GWXXaa1yfUbEhKiW7tcdWZ4NikPPDw8EBwcjIyMDPnuhx9+GIB9mx3XSmhoKACbpUy/z/POr7/+qttsab8LFiwQw4d7Idfkb7/9Jp0r3rx0/PjxM3RXAJsNNmLECADOWC8vL0/jLw/8/f3lsxs1aqTfAdisXfp2MgZHjRqld0+2UEFBgdjaZBQeOHBAzCDXG8IAO07lTalkeTVs2FBxK3WUFi5cqN9LnbN+/fopTnAnTHLmIgLppo888ggA5wFixowZbnumPwPLsuRg6EC2b9+uFic6PjqIbdu26XukqxUUFGjjL8cV2gYXCBeLjf43XOzjY5sB1x6TNe3atVOgxgQG12JNAwM2+hTSbrdv3y5hUgbie/bsccMT/m9gsMdgmkH35MmTlYgi9bwmtv3wWli2pdEud+zYoeDatZWrJoJBLgUfeZ17//791ZLGsU6fPl1JDndi+/btuoKXhxAe2gBnYJ2Xl6erXCmyySu4x44dq1ZKUu85z+5G27ZtlVTilfTBwcFqN+A6S0tL0+GdSZmXX34ZgN0ywIMGD1uk5VcHbN68WQlpJgiPHTumNgAeevr164cpU6YAAN59910AzjjM19dXfobJjuqSgIqIiMCnn34KwNlqVlRUpEM+bW/y5MnyjUy+cd9o0KCBWu8o6kx7djdWrVqlw7brhRoUkWWRs3///tofKJ7KNqj69esrOcBxWpal6+CZRAgKClJLMIVY58yZU+XxePfu3bUHMKmWm5urljTGJ0lJSSoCc93y4N+oUSOtO67N/fv3y9cwkbx161aNl9cv33rrrYr7yoOQkBAMGDAA+fn5iquYWFi7dq0SGxTPdjgcEr5lcuLgwYOKt7iW7rnnHtkoW7n+8Y9/ALCT3rRpnpM8PDwU3/BrBw4ckF3Tf2VmZip5Uh6UlZUhLy8PWVlZ8m1sFVuxYoWem0maNWvWKK6iX/Hz81MrJMWYvby8VGhiMomtd1lZWUpM8fOxsbHa93v37q2/xbYxXhKwatUqzaU7YdqaDAwMDAwMDAwMDAwMDAwMDNwIw5y5SBAREYFXX30VgLMliCJfpCpWd7AKHxoaqso0KyzBwcH6Gq/xIzV9165douMxK+/h4WEYM9UMF4ONng//F8bHig3XHkUuc3NzReslU6EmXjMdERGhaidprqyyvPfeexobW9TIrqkpiIiIkK9kxYrVtwYNGqgNjdUmVtJqCiIiIkS5Jw2b10w3bdpUlV5W2gYPHqx2vJqCiIgIVW65x3HtrVy5UlVNttgsWrTIDU95LrKzs1V9JduAArqAU9zyk08+0RWqbLd4/fXXAdhtImyhodBsdRFY/+2338RWIlU+ISFBovD0nXXq1FFl2rXaDthVW1bdKVY6d+7cCzSCP0aXLl10tTCZJZGRkZoftvFu3LhRbSSsflMw9eabbxaDiq2j8+bNu0AjOD+aNWsmNhoFUHNycs65fjo5OVnxKllSw4YNA2Dv/bwOncLjZHC4G1lZWWqFIXvtxx9/VJzNto+ePXtiyJAhAJwMNTILlixZovkju6tjx47ytxTDbdWqlVgJZJds3LixyluB33nnHbF4xo0bB8DerzkHjM0GDRokphAZF2x5ioiIkN3yv+np6Wp7uvnmmwHYDDG+J7LG0tLStKeWBxkZGbj//vtRt25dCaWT/bJmzRq12LCboHnz5oiIiADgbDnr2bOnWF1k+np5eSk+4fxS/DYmJkZsIa7LGTNmiFVEMf38/Hz9DjJXxo0bp+vIy4OioiLs2bMHKCdTjAAAIABJREFUffv2VZsS3+ngwYMVj3DObr/9djz33HMAnP5kyJAhisvIJGrYsKGYM2xNpr01bNhQez19bGFhod4l319cXJz2GP6tQYMGKfZzJwxzxsDAwMDAwMDAwMDAwMDAwMCNMMyZGg5WYL755htVOZn5p6hUdQcrEMSpU6dUmWaG09fXF4MGDQIAvPjiiwCcmgkHDx5UZp5VJyMCXH1wMdjo+fB/aXwff/wxAGdvOgXm7rnnHrz33nsAnP3106ZNqzHsGdcxUtSSDAxW5ouKiiT+x4oNGSbVHa7jW7BgAQBndY5VsGPHjomJwQpeTdFkcR3fE088AcDJ4GIFPC8vT5VDVgFpszUBrmMkm4L7HNkyP/zwg/r5KQKZkZEhlok7sWbNGjFL+P4vueQS6RCMHj0agK1twep7fHw8AJuBAthV1TfeeAOA84p7igu7G+PHjxcrgVp5KSkpenYyLE6dOiUGBmMfau8cPXpUArpcp506dZKopbsREREhhgRjszlz5kjPgwyEwYMHi1VJthT3ggULFmifpHbS9OnTxTxxJ9atW4e//OUvAJw2FxAQILYFdVcKCwtlw3wPZADUq1dPvpUsb+q7uBvBwcESMuYa2717t7Sd+JweHh4Sfh41ahQA4N///rc+T/YLr8j++OOPZctk1WZlZYkpQd/Vu3dvsRuqCl27dpXeEcXs165dKxslk6KgoEA2Sq1AMoJ+/vln7RvUbbEsS0yVp556CoA952RFk3XStWtXjbc8yM3NxVdffYWlS5fq2cjmiIqK0v5MPxcQEKDPUZtl8ODBYuuQWbJ+/XrtD9zXKaLu7e2tsdN+09LStD9SC8uyLLEA6b8okFxe+Pj4oH79+ggMDNRz8HcVFhYiLi4OAPTfDRs2yD+SeX7gwAFMmjQJgFM/JyAgQKLSvM6eTMz169eLBca5iI6OVlzOuXrooYf0TnileO3atcXo4zXi7oBJztRwcBNgIAo4RZ/oSKo7uHjoSKKionTo42YxceJEfPDBBwCc9NGPPvoIgO1A6MD4OyzLqpIEjaO4BCUHs8r9+c/+8nm5P/tExpAKPYtPdvmphVWJBQsWiLLNzXr48OEKvBgEJCQkyE5rko2+//77curcDIOCgkSBZQD24osv6qafmjS+wYMHq1WAwbVlWQriSF1OT0/Hv/71LwDONciA+/eSMNWpleuFF17A4sWLAQBDhw4FYPsKJoEpRjd79my1iLCtgHZcVFSkAKldu3YAnBRid+OWW25R0MuDer169RRsMZm0bNkyrUfSmklv79Spk5LcFAGs6kC6vLjnnnt0WOIB+Oqrr1YwTXucO3euBFg5V7ydpFu3blqPbLmoLu0GADBlyhTNBQsR8+fPV5KF6/Ctt95SSxqDaAbkubm5onOT5l5dRJ2bNGmioJtJF44NcCZ6GzZsKDFOrkEejj/44APFPBR3rC6CuWlpaSoOcU8IDAzUYYJr8Oqrr1YrAeeUc1VcXKxDLtslqpMfDQwMVKzFxPQzzzyjNjsmbgBnKwXjsM6dOwOw9xgefNlmUF3aJw8cOKDDKttZ69WrJ0HbFStWALD9I/f/zz+3Yzz63cTERM0v44DqIggcGhoqsVwmBJs2bYqlS5cCcCbSevbsKd/CAzD/v2XLlkra85ajqKgoPP3003/49/l3qhKzZs2SsC8vMOjevbv8y6pVqwDY65B+lvEdpROaNWumWG/mzJkA7HfCZBPX5ssvv6z95vbbbwdgt0OzeFURMHn0RwgPD1crLv1NXl6e7I8Jjbi4OBWTuI/zMhUvLy+NhUmR119/Xa1rrucoJob5rt5//33cc889AJzv8nxwOBwoKSnBd999p72J8eauXbuUhKYIclhYmNqe/va3vwGw90Em8V3lLSgiz7ZKJqjy8/OVZGU7WEJCglq0KOz89NNPK5lIW46Li6sWl1mYtiYDAwMDAwMDAwMDAwMDAwMDN8IwZ2oomCHkdYYAcN999wFwijnVBAQEBCibySrmsGHDVG1iJeaJJ57AK6+8AsBZlWJ219PT8xxRQNPWdOHg4eGB5cuXA4Cy1U2bNlXVnpXuDh061Egb3bp1qxgSrFCXlZXJRrkWR44cWSPH5+Pjg4ceegiAUwR306ZNqpixihEcHKwqbk0aHwC8+uqrePLJJwE4xUj9/PwkPEoKd7du3VTdYXWfFNywsDCJxrFiRPqruxEWFoaxY8cCgESpd+3ahRtvvBEAVCVLTU0V4+C7774743vt27cXC4EVx969e6uy5U7UqlVL1ULOY/369bUXkOYdGRmpChsrvax6FxcXixF03XXXAYD2lOqAgoICVe1ZrezXr5+q7hRgjYiIUHsQq7vcQwsKCuSPyE6oiHhjVaJWrVpqFSTTIjs7W/RzMtSWLVumlmUyZp599lkANqWeVHNelco2IHejSZMmqs7znZ88eVLsX87RTz/9JMYMmQqMV06ePKm5ZdX7kksukS92N/bv3689kM+Znp5+hr0CtsAv2+vYokEmzfjx45GamgrAeaUuWXHuhmVZYi6RObpo0SL5EMaj1113nYS2b7vtNgDOWDwqKko+9q233gJQfUSri4uL5UfIHhw+fLjaOzn2LVu2IDg4GICzFYafOXLkyBnXOgM2G7U8zJkLgb59+0pclizR0NBQ7W1kaTVs2FCMb457+/btAOxOBPoeMjoSExMl9EwB60ceeUTzznfRsGFDMf6qoq17wIABYgZNnDgRgO0nOZ/8r2ubMpk+9E/+/v76HYzpYmJiFNfRT0dFRWHGjBkAoPmdNWsWnn/++XI/b1FRETIyMvDDDz/g8ccfB+B8f7feeqv8I/fwU6dOyfeRJRkeHq72Ks5pQUGB2kf5ee59Xbt21djJfOrbt6/mnowbXvMNQPa+f/9+xQnuhGHOGBgYGBgYGBgYGBgYGBgYGLgRhjlTQzF16lQAOOPKtu+//95NT/PnUVJSoqoCRSh37typbCYr2zNmzFD1jH3KzHQeP35cmW/DmLnwOH78uERUOZfr1q1TRZ5ic1lZWTXSRrt16yZ75HWvmzZtUtaeOh2rVq2S5k5NgpeXlxgzFBpt1qyZBDdZnQ8JCakWVwz+GUyZMkUVF2ollJaWSjyWVZmGDRuKwUf75fgdDof0O1iZI9PB3SgoKBALhFd6dujQQXor1Bd44YUXpItEvRbacYMGDSSgyGuYq8v4wsPDxWAi26BNmzbSKyMTY+bMmarqsSLH/vFHH31U4/vwww8BOK/Urg4ICAjQtbWs1u7du1esPTJFQkNDpd3Afnruk0OGDBFzilVe6ry4G5GRkfKfd955JwDbj5LhRPZFnz59NK9kabz88ssA7HfQo0cPADiHZeRunDp1SjEJfUbnzp3FomGstnTpUmlWUQeLa/fBBx8Uq4QMMDJOqgOioqIUf3Fd5efnS7+KX2vatKn8LH0J98mQkBAcOXIEgDOWY+znbowZM0ZahvQpgPOKafrKrKwsxTXUnCEzo6ysTGOmgPBHH31ULWKf7t27S3OG7K1t27Zh2rRpAJwMpo4dO+LTTz8F4GRD8TKA2NhYvQdqd5BlWl1A5gd1jvbv33/O2aCgoEB7BPd1Mku+/fZb6X9RP2b//v1iRJE1tm3bNr0L+qMhQ4bgr3/9a9UMDMDy5culDUNdteHDh8v+GLelpKQolqHWI4VwCwoKxJIhc6Zjx47yzxR8Lioqkm3w/OXj46P1TvbY+UB2SmxsrDQNaXsbN27Uc5P90qJFC8Vq9H3jxo0T05esyuuvv15xOQWdyYTq27evxsrrzz/66CPpA9E3JSQkSEyYgtdNmjTR87kT1cfrG5QbvXv3rtLFfyFRVFSkhcAgJj8/Xw6PVPY777xTC5sOwVW0ijDJmQuP/fv348CBAwCg9qYpU6bowEsRzupCr68o8vLy5PRdD74UjiUds1GjRjUyOVO3bl0FKRRbCw4O1rxxg+zfv3+NTc6EhITosMOx7t+/X3PIsQYHByugZvsEDw5XX321NnzewkXqrLtRVlYm0T/uDUFBQRJuZMD2+eefS3yUh3beOhIQEKCgjEE6BaDdDS8vLx3oePD96KOPdDsDxRA/++wzPTuDTiYwevTogSVLlgBw0sF5EK4O8PT0VHKGYzx06JCSUWwvfOWVVyRWSiFLtmD4+voqIUeRxOqSYFu5ciV69eoFwCnmX69ePYwYMQKAk+retm1bJUaZuCEdPTs7W4H12TR+d2Pnzp1qcXjmmWcA2M/GAxLtcNiwYTrUcm6YbOvRo4dEyikQykNJdcDRo0cVb7F146efflILCFt909PT9X0WbGjHH374ocRFedjlOnY3nnjiCT0b94uCggL5Ho6ztLRUY2X7IFti2rZtq9ZYviuuZ3fj22+/1f7HfT05OVlris995MgR+RiKINOOy8rK5KfY/sFEf3VAcHCwkrpMRHXt2lX/ZtJt8+bN8i88tNPHAs6543tq2rSpildMnl9++eVqk6adr1y5Ugd+JhwqE+3atVPiiO12d9xxh5IoTFb/4x//kO/gvsf3cvDgQY2d46tVq5b+zSRVenq6/BBjvxYtWlTI53p4eCA4OBjXX3+9WhyZVAkKClL8TGFey7Jw9913A3C2j3t6eipuueuuuwDYex9bRZn4pSjzpk2bdN6grY4aNUprlcm7devWKSHF99eyZctqkfA3bU0GBgYGBgYGBgYGBgYGBgYGboRhztRA9OnTR9lvYteuXcpG1iR4eHhoLKRvp6amqm2ANFl/f3+JkDFbzIxnWVmZKhrVpZL9fwlXXnmlRENJDy0sLFSFYvz48QDsLHhNtNGgoCBVblmhDwsLU/WTbK0mTZrUyPGNGDECo0aNAuCsPBQUFKjlg1eGrl+/vkaOD7DtktUSVki2bNkisVX6Dy8vL7z99ttn/CzZJFu2bNH8s8JYXa4pvvTSS8U2ZEtIcnKyGDOsxl9++eWqMvHzZJgUFxdLXJVU7vbt21eLqu/atWu1BklT3rJli9oHOA+rV68+o9UXOPNKX9ovK728irQ6IC4uThVJVnJ/+uknVXDJ2hoyZIjsle0EpJk3aNBAv4NVbTJO3Q1/f3/5StrZpk2bxEZktfndd99VhZPVaLKhLrvsMtn3rFmzAAD33nuvro91J44dO4ZHHnkEgC0iD9jMILbjUfz2jTfe0BXiXJe0yxEjRqiNhONs3bq1WkzcjWbNmqkSTVZAdna2bI7rafDgwWq5YAWb4wkKCtL36EeHDRt2gUZwfgwYMEDj4jNNmzZNraBkLOTn54tdwLnjmoyIiJDP4X+vuuqqcl07XNVwOBxqGSF78IcfftAewDW4cuVKrSkKCNNWp02bJlbpzTffDMDZTlId0LlzZyxcuBDAmdcuc+8mW23lypW46aabANhrEnD6m4iICF0xTQZVmzZtxErlZRdpaWk6v7iyAauSzff444/j3nvvBeAUx7377rvPYKPwechIIzuR72Pnzp3aTyhy3bNnT52pyHCJi4tTCxfbahcvXoyhQ4eW+3lLSkqQlZWFAwcOyHfwvwcOHFCbElFUVCQ7ZFz63nvvqV2ZzNA77rhDeyNbC8kCjoyMVFsvbTUvL0/7Ja/odu3cIIvWw8NDbWDuhGHOGBgYGBgYGBgYGBgYGBgYGLgRhjlTw8EKxIABA1QZrklwOBzq52VP8rFjx1SBof7Dzz//LOEoZohdNWcMY8Z9+OKLL3TlHYXzCgoKVB1k5jozM1OVspqEefPmSXeE/dejRo1SJYHMkrfffrtGju+XX35RJYHstNGjR6v6y+vRY2NjMX36dPc85P+IY8eO4Z133gHgFIMNDAyU5gFFRouLi9V/zzknu6Z169ZYuXIlAGc1mNU4d2PHjh3Sz6G4KK9Hd0Xfvn3lZ1nJ5TjfeecdMadoDxT6djeuvPJKVcUofvvYY4/pimFehb5r1y5pC1AX6M033wRgiyKyb51CpNVJB2vlypW6TppsrRtuuEE9+dQ2aNWqlb52yy23AHBebZ+WliYtIV4zTpFad6NLly5ab6zaDh8+XH6GFcyioiKNgfPL9elwOKSzM2HCBADlE6W8ELjllltUPSfzt1mzZtLq4LXK0dHRYq2xos3qcV5ent4HNT8Y91QHeHl5yfZYaa5Tp47YBe3btwdg69FwHNTnYLU8KCgI/fv3B+D0n2ShuhslJSV639wLDx8+LH0OVuN37dol9h2Ffmmj2dnZEnzmmAcNGoT77rvvwgziPIiOjtYz0eYefvhh+UzuffXr15dmEJltl19+OQD7qnsyC2jHZBFVB3z88cfSO+ScfP/992KIcJ9s27atfCNZMjxvfPDBBzpbUSfL29tbexDZcG3atBFLhn/r5MmTesdVgfvvv18MEDJ+fvrpJ7EL69SpA8DuMCBLiCxosmBvuukmsU2o9bJp0yaNhdds9+vXT8xL6mmFhYVVyCdRc6Zp06ZaU9dffz0Am/3IcytZTEuXLtVzU1+mTZs2mDNnDgAnQy0pKQnPPfec3gkAnDhxAoCti0P9R9r22LFj9TWeLz/++GOt6aysLAC27fNvuRMmOVMD8eyzz0o5+2IAqWlcMJ6enqIX0oG43urERIyr+K+rKLDBhcXQoUPx2muvAXBSgWfPnq1EDTeS6tICUlH4+fnp0ErRvzVr1siB8yDBFryahnnz5mH48OEAnEFKYWGhNmDOX3W7kaEiOHXqFJ5++mkAztsn3nnnHVHtGWT26tVLooDcrJkMPnLkiBI1FMljosPdCA0NVYL0tttuA2AHZ/SfvN1v48aNOkgx6Fy9ejUAm+rMAyHFWbt06VItRKATEhJE0abQ7Zo1axRYMXAMCgoSDfuTTz4B4BQLjIiI0OGRgrOk7FcHPPXUUwo8ubedOnVKyV/aYZcuXRRkM1nMnxs5cqQOhkxsVOVBoSJo1qyZ2kOY8N20aZMOFVyLN9xwgz7HQwXbCHbs2KF2WQbwbdq0kaCuO/HVV19hzJgxAJzPGxYWJiFgHmBjYmKUjGA7Gg/AI0eOVGKHX6suiQsAyMjI0HxNmjRJX2vRogUAZ4tEdna2hHV5IGJL0Oeffy7fenabqLuRmZmp9UNR0Pr166NTp04AoLa1oKAg2aurkClgt0xSnJ1Jgupw+wtgC1RTFJbzOHfuXCXo2c7SpEkTHdSZXKSobGRkpGI5HnxHjBihNetuDB48WEK4bPuJjo5WsnDIkCEA7PYcrjGeKWir27Zt017I9bhw4UK11jLx3bp1a10mwIRzRESEPlcV+O2339T2Sn/Tp08fxZ+0x++++05jZSGGLb/btm2TnVOS4Nprr1XilWOKj4+XHbj63Yq0yp46dQqJiYnYt2+fno0J9hEjRqhYxra5v/zlL/Ij3OuXLl2KcePGAXAWnzZv3oyZM2cCgC4+YFHprrvuUjKU+39iYqJ8Fu0iPT1d50radE5OjhJtvKHMHTBtTQYGBgYGBgYGBgYGBgYGBgZuhGWuHr6wsCzrMAD3Kyz+OTR0OByR5/uAGV+1xh+OD7j4x2jGV61hbBRmfNUcxkZhxlfNYWwUZnzVHMZGYcZXzVEuG60KmOSMgYGBgYGBgYGBgYGBgYGBgRth2poMDAwMDAwMDAwMDAwMDAwM3AiTnDEwMDAwMDAwMDAwMDAwMDBwI0xyxsDAwMDAwMDAwMDAwMDAwMCNMMkZAwMDAwMDAwMDAwMDAwMDAzfCJGcMDAwMDAwMDAwMDAwMDAwM3AiTnDEwMDAwMDAwMDAwMDAwMDBwI0xyxsDAwMDAwMDAwMDAwMDAwMCN8HL3A/xfg2VZDnc/w/+AbIfDEXm+D1zs4/P2CXT4+YWX+xcWlf+jsEqt8n8YgE9mfoU+fwJH/3B8wMU/h2Z81RrGRmHGV81RJTbaokULnDhxgj8LAPD09ISvry8AwMPDrqXl5OQgJyenYk9ccVT6HEZFRaG0tBSAcyyFhYVwOBz8ffpvaGgoAOh9FBcXAwB8fHzg7+8PACgoKAAA+Pn5Yf/+/RV5FKAKxle3bl2cPHmSPwvAnj8/P78zntff3x9lZWUAgLCwMADAgQMHANjjjYuLO+PzZWVl2Lt3b0UeBagiG61Tpw4CAwMBAN7e3vYfys4+Zw4DAwORl5cHAIiNjQVgzzUAHDp0SN+jbfv4+ODIkSMVeRSgCuYwOjpatsZn8/b2RlFREQCgpKREXyNOnToFwJ5r/hw/x58LDQ3F7t27K/IoQHniUW9vh6+vL2JiYpCVlQUAqF27NgD7fXM+uGaOHDmiZ+N/a9euLXukXwkMDDxnrXp52cdFy7Jw/PhxAECtWrX0uzIzMyt9fADg6+vrCAgIQFhYGHJzc894prCwMK0T2mBxcbHGVq9ePY2b9sfveXl5ISAgAIBtkwDkd7y8vBAUFATAOYcFBQU4fPhwpY8xKCjIERERAcuyNF9cHwEBAXpuwuFwICQk5IwxW5Z1zpqyLOucuebnAwMD9d6OHTsGwJ7LqpjDgIAAR2hoKAoLC7VuaDeu+xi/l5eXp3fPZzx16pTmhnZZVlZ2zl7AuQoPD9fnuD5DQkL0Of7X09NTds3/FhcX69/79+8vl41WBUxyxqAi2OPuB6hi/OH4/PzCcUnXO8v9C3eP9iz3Z72Plf+zANB4+roKfX6l45OLff4AY6M1HRf7+ICLf4xmfH8Cb775JtasWQPAefioVasWGjRoAAA6SMydOxdz586tikdwRaWP8frrr9ehjgf8Xbt2KaBmEsPb2xtXX301AGD16tUAgIyMDABAo0aN0KZNGwBAfHw8ACAuLg6PPfZYRR+n0sc3adIkbN68GYBzLKGhoWjevDkA5/O2b98e+fl2YWXEiBEAgCeffBKAPd558+YBAJKSkgAAJ0+exG233VbRx6kSGx0/fjwuueQSAM6D73vvvaeEBg81Xbt2xdq1awEAL7zwAgAgOTkZgG3nP/74IwCgSZMmAOx55bgrgEof45QpU3RAbdGiBQA7IcXkGA+TMTExOvxt374dgDPR1rRpU31u3759AIBBgwbh+uuvr+jj/OH4fH190bFjRzz55JN46aWXAACTJ08GAKSlpcHHxwcA0KpVKwDAggULcPToUQBQMmzChAlKAHzwwQcAgB49eujQTluuU6cOAPvQv2rVKgDATTfdBAA4fPgwpk+fXunjA2y/N3DgQAwZMgRfffUVAOjwfu2112pdcT7279+vsT3++OMAgPnz52PPHvvPZWdnA7CTUh06dAAAvPrqqwCAIUOGAAAiIyPRvXt3AM7EaXx8PF5//fVKH2NERAQeeOCBMxLxP/30EwCgQ4cO2LVrFwBn4rO0tBT9+/cH4Ey2eHh44OeffwbgXFO+vr4aK5NPTMJ17doVCQkJAIBPP/0UgO2/HnjggUofX2hoKCZNmoSUlBTUr18fgNNu5s6dq3mLjo4GAKxduxZ9+/YFAGzZsgWA7Ts4N9xDjh8/jo4dOwIAtm3bBgBau6NHj1Yij+tzwIAB8qmJiYkA7IQNk5nh4XYlPSsrC1FRUQCAhx56yG2xjEnOGLgddDquWWD+28DAwMDA4GLHiRMncPDgQQBAp06dANjBKSviPFQNGzbsQiRnKh3+/v6qYpJh4uPjg2uuuQaA8wBRVFSkQ9iUKVMAAAsXLgQADB06VIclVrF79ux5gUZwfnTv3h1169YF4DwsXHLJJToIBAcHAwCeeOIJvPHGGwCArVu3AgAaN24MwD5U8DDBAxqrw9UB/fr1w2effQbAeRgePHgwPv/8cwBQUi0tLQ3t27cHALz77rsAgC5dugCwD75MfFx11VUA7IPZn0jOVDp+++03MX14sPXy8tIhfunSpQDs6j4TMGlpaQCgzxQVFekQPGDAAADOJE1lIzg4GL1790Z6eroOqvQTXl5e2LlzJwAgJSUFANCyZUs9y8iRIwHYSQcmjphQ27Fjh5LC7dq1A+BMGBcXF2PUqFEA7KQa4FyfVYGQkBBcfvnlOHz4sJJ/RKdOnXRY5zopLS3V833yyScA7CQwmU2dO3cGYK9XJiiYHOVZpHbt2jro8x1y/JUNDw8P+Pv744cffhBrjgmZvLw8NG3aFAAwcOBAAPYcpaenAwDWrbMLtJGRkUp8bNy4EYBtB1xftGX6md27d+uM9e233wIAPv744yoZn5eXF6KiotCnTx+tn8WLF+sZmUAkm2bAgAFK2DOJP3PmTM3RoEGDANj+/8svvwQA9OrVC4CddALs98LkIvec9957T0luJiszMzPx66+/AnCybvLz8xEREVHJb6HiMMkZA7fAlcJ89tcApyN0TdKcTZ39owRORT9vYGBgYGDgDhw6dEiHCleaPenqDDyvuOIK9zzg/4j69euLjs/K9qWXXqoAnImpSy+9VAd5JqYYfC9ZsgRjx44F4KwQ/wkqfpXg888/F6uHc5WUlKQDLJkVU6ZMwc033wwAmDp1KgCgYcOGAOwqNg/ZPBwzCVIdcOjQIbUXsOK8YMECtexw3CUlJWJasFr9yiuvALBtmgdN2jar5u5G3759xVAju6dz585KeDDRdNNNNymeJDPo+++/B2Ana0aPHg3A+Y7IzKhsOBwOOBwO7N69W2wsHkaPHDmif/PgXr9+fcXWTBZ6enritddeA+Bs6/Hw8BBzhEkPHl7j4+N1yGcS9cYbb8R3331XJWPMz8/Hxo0bMWzYMDGY6Ecef/xxsWg2bNgAwPaPXDN85gkTJuC3337T8wNAamqqEk9MksbExACw2RZcy2ytHDhwYJXMY0FBAX777Tc0a9ZMPpD+7tixY0rucn4vv/xyfY3jDAkJ0Tvh+2jTpo1+H5MRXIspKSnyOW+++SYA/Jm2wnLh5MmT+PXXX5GcnIzevXsDgJKBs2fPVrKd9vb0008rQT1jxgwAdqJ65syZACA7O3nypPxNt27dADhZQGFhYWIXMYlfXFys8V966aX6GveT9evXA7CZYmTduBNGENjAwMALFT/MAAAgAElEQVTAwMDAwMDAwMDAwMDAjTDMGYMqhytz5WzGDKmG/w2ubBdSRV0ZNmd/5o9+h4GBgYGBQXVDenq6KN6skp48eVLVXLJJZs2a5Z4H/B+RmJh4jtjtli1bxB4hrb5FixZqr2A1l9osmZmZ0o6gBsFzzz13gUZwfvTq1UstP9RjcW2PYGXW4XBIn4PtC2Qx9OrVS+LGbJNZsGDBBRrBH2Pfvn1qSSMTITIyEtdddx0AJ9MpOTlZbBPaNNkyhw4dUlsGq/1kqbgbzZo1U2WerJO1a9eKZUDmRHZ2tlgZd911FwCn8HGzZs0wceJEAM7Y86GHHsI777xT6c/r4eEBPz8/hIWF4ZdffgHgZLoEBQUpViYTYN++fVi2bBkAm4EB2C0mbMe79957Adjzx7XXtm1bAM7WqOzsbP0trkG2D1UFvL29UadOHaxdu1ZsLJ4bwsLCxH4hW2nhwoVijFx22WUAbDYTbY6YOnUqPvzwQwBO5gWZOQkJCXp3ZGdQ96qywbafdevWyT+S6bJs2TLZHjVRfv75ZzFQyDrs37+/mCX/+Mc/AACbN29WCxvZNLSNSZMmibV2tnB3ZSMoKAjdu3fHiRMn8NFHHwFwihAfPXpUrK7//Oc/AIDmzZuLFcmWo3HjxmmuOA9du3aVxheZXNTsGT9+PIYPHw7AydJxPX9ynsvKytSCGhlp6/4mJiZqf3InDHPGwMDAwMDAwMDAwMDAwMDAwI0wzBmDKsPv6cb83mdchcbOxvl+9mwGzR/BsiyxbwwMDAwMDKoL2rVrp1tTyDYYNGiQrkFduXIlgOolEFsRtG/fHsuXLwcAaesAkLixKzuGOgAUt+T7cK2WspI6adKkasG82L59u1g8zz//PAC7Kk2NIGpXbN26VXEIGTYU0t27d69YU5s2bQLg1KOpDggNDZU+B2/xSU5Olj4On7lr165ITU0FYOt9AJB4Z3Z2tirdvI2F7BN349ixY4orKTS7f/9+icKSneHj46NKO0V/+XPJyckSuSbDo6rEVr29vRETE4NTp05J6JZ21qtXL80VWRH169eXlgoFpydMmCAmwT333APA1kUi84nsC7LCGjVqhGeffRYA8O9//1ufJyOnslFUVISMjAx4eHiIzcN3HxcXJ9t7//33AdgMLX7/xRdfBABcd911umWMjKHly5eLocH5db1xi6Ls1P8ii6OyUVxcjMzMTPkAAGLBXHbZZdJA6tevHwBgxYoV8os9evQAYLOFeI7iPGzatEm+h3pIZKz4+PiInUiGCW/7qmwcPXoUn3/+OXJzcyVQTB8XFRWltUHdqcWLF+P2228H4PSP69evF6OQe6Sfn59EgmfPnn3G+O677z5pQf3www/6HsWVKSydlpamr1Hgu3379tqLqkorqjwwyRmDKsPZQryuaueuquv8Nzf7U6dOiTJKOBwO/TwDG9ffd3bL0+8ldUx7k4GBgYFBdURSUpIERNnusmXLFrRs2RIAJBT797//3T0P+D+ioKBA7ROkpq9cuVJCsTwceHh46HYRb29vfQ2wW7vYbjJu3DgAznYLd+Oaa64RbZ/JpZ9//lltWGwt6dGjhwR0d+zYAcApSNqhQwe1YrBVhFdxVwccOHAAzZo1A+BsqRgzZoyen/NUWFioW7Tmz58PwDnn/fr104GJh0smHt2NV199VW12fO8Oh0M32vzrX/8CYCfT2O7C+eX1u+Hh4WjdujUA561kVSW26nA4cOrUKSxatEg3mzEJVlpaqpYRtvQkJCToaxRgTU9P12GZX/P29pbf4W1UbF2Lj49XUo02wEROVaCsrAx5eXk4fvy4EhRMomzcuFHvmM+yfv16JcyuvfZaAHbCl+cLHtoLCgrU7sPECK+jLigo0Ppj611VJUmLi4uRlZWFevXqaQycy507d0owl9ec7927V+Nn8q+kpAR9+vQB4LTHyMhIzTttmS1MderUUaJk9erVAIDXX39dNyFVJvz8/NCiRQvs27dP/oHrYceOHUp48lkfffRRnefY6lRQUIC//e1vAJy+IicnR8kZzg0TcC1atNC+wJuXNm3apBvkOObo6GglNZ944gkAZ4oruxOmrcnAwMDAwMDAwMDAwMDAwMDAjTDMGYMqA1ksZMZYlqV/k8Xi6emp7C//6+vrqwwyKWyuAk38Hq+RKy0tFfWPnysqKvrdFiZzrbaBgYGBQXXDnj17xJxhNS8nJwcJCQkAnK09t99+O2699Vb3POT/gHHjxmnPZvWzefPmYhmw3WDXrl2imrPdgBXUhg0bijlDYWQKgrobx48fV3xDJkJOTo6el8yntLQ0MYjOFuVs166d2DQcF9tPqgPS0tLUksRrab/55hsxGthWsH79el2Hy5iMrTA//PCDmEVsmaku16FPnjxZrTocU05OjlgXvHI6JSVForMUFCULaPny5argk+HQuXNnMYgqE8XFxTh48CD69OmjthC2+SxatEjXCA8ePBiAzfwhU43stMTERLU9sf2lZ8+eYqSwjZJsp759+4pNw+8xJq8KlJaWIi8vD/379xebx7Xlir6BrZJt2rRR7M/1WFxcLEF1zlvjxo0lvMv3xHNEy5YtxbjgWGvVqlUl4/P19UWjRo1Qr149fPHFFwCcV7AfPHhQDDSukZCQEM012XgJCQmyOX7++PHj5/gestdWr16t1i+2dnF9Vjby8vLw888/o379+vKLXE/Z2dlaF5yf559/Xm2OfMb8/HxdY0/7jo+Pl2+hQDJb0FavXq12qcmTJwOw55RsqH/+858AgHfffVctUbSFvXv3VovWYcOcMTAwMDAwMDAwMDAwMDAwMHAjDHOmmoJZ4ClTpkgIiVnQDz/8UNlf9qZXR5x9bbbD4VDljNfz5efnS8CM4k9ZWVkSQaQoGwD1yoaGhgJwVm6Cg4MlJsyr6Hbv3q1qVGWyZIpqO7D3ltJyf/7ZLkvK/dnrgyvWt9slbVqFPo+3K/e6wz9jo+zdZlabWfC0tDT1CVOYq6SkRAJ17gDHd/z48XMy80lJSapu025nzJiBRx99FIAtCgjYva+AfVXjp59+CsApwObj46Meb3fAtdLEKi2r83PnzsUDDzwAwFnVTUxM1HWwvEKT856Tk6P+bVZzcnJyquT60IqAY7zjjjuki8BqrZeXl77Pa2GPHDkif0R7ZC/3ihUrsGjRIgCQf8rMzFS10R3g84eEhEjAkHoW+/btk6/kFaT9+/dXZZ7rkpXHmTNn6t2walq7dm31v1cmmjVrhhdffBERERESWqRYbGhoqJ5x6NChAOwKG6umtNHvvvtO+hys5i9evFj7DfcCMjM+/PBDXee7cOFCAHbFlBX+ykZUVBRuvPFGdO/eHUuW2PsAr/XcvXs3+vbtC8DJDtm6datsjiK5hw4dksYHGRRHjx5Vrz1FHdmv37t3bzRq1AiAc369vb3FBqhMNGzYEI8++ihOnDihSjXZBlu3blX1lz5i2bJlsisK+I4aNUoiutTw2LBhg0RLOXZekerv7y+b/utf/wrA3kPmzJlT6eOLjo7GlClTEBoaqiuiL730UgC27TGW4T6QkJAgv09/eujQIb0T6llceeWVEs5lZZ/r89ixY3jjjTcAQPbO71UFIiMjMWrUqDP8N+dhzZo10lHh+iopKdG4OecdO3bUtd/0nT169NC/aYfUFOrUqZP85w033AAAePvtt6U9wfV7yy23yL7J8vD09JSfmzRp0h+Oz8/PD82aNcNtt92mq4gpbDthwgRVzbnuYmJipEvB8Y0ZMwZr1qwBYLOEAFuolSK6tFXa6IkTJ/SM3DszMzOlGULflpubq3HxnbuuGzJczoeioiLs3bsXTZs2RXx8PACnsGphYSH+8pe/AHDuCX379tVV4WRY9OzZU/6JQtbz58/HmDFjAEBri3oh27ZtkxYUbWHZsmViJTAWT0lJEXOItpCZman1csstt/zh+AA7vu/bty/27t0rf8C9fPr06bo6mv40Pj5efoZsiF9++UU2R7ZFdHS0NKL4fLTHpKQkrFq1CoBz/3C9CppMm82bN0vPhc/297//XftMedhSvr6+aN68OYqKisTWoQ0WFhbK7w8bNgyALe5MPRXGHq1btxaTkGLUTZo0EQOROkTca/r16ydfRWbm6NGjMX78eADOuHX69OmK9WjfSUlJOnuVh9Xn7++PVq1aoXfv3lozjPu//vprMXa4J3/44Yfak3nFeWZmpuaP/jA/P19X1tNncHx5eXmKW+hrY2Nj8dhjjwFwnlOCgoJky3y2a6+9tkr2k4rCJGeqKRiwMNByxdSpU+VMEhMT/9Tvz8jIkLgZbz+oTLjepOSaHKGzZjBZWlqqoICUu7KyMv08HQLgXFA8DJMax98DOJ1saWmpFh03WX7GoHLwZ2zUdX4AaPPYv3+/gnjSg1NTU0V1rAob/SNwfI8//rhotEwQxsXFyV5pZ88884yCLW7Ob731FgA7Gcmx0h6XLFlSLcb37bffSiCNrQUPP/ywaMwcX15engLHFStWAHDSmZs0aSKRQB4cP/nkE7eOD3CO8a233tLBigHbV199pWCb4nhLly6VuByTaXw3BQUFslvSbZOSkqrFHP7nP/9RwMSDYYMGDRTEcUyJiYlqKWEA9vjjjwOwafA33ngjAOctOVu3bq2S8eXk5GD+/PmoU6eOkrj0I4MGDZIgPBM2tWrVUisIA9IrrrhCY2bSNDg4WEEWA13SwS3L0ruhH2rdurUCtsqev8LCQuzatQupqam6/YEHo5SUFAQHBwNwBpSlpaW47bbbADjtq1evXvoZBt0HDx5UApTrlbT1oqIiHbSYxAgPD6+SOSwoKMDWrVsRFRWlwyUPgWVlZTpAMZkWGxur98+k9IYNG3Tw5+EiIiJCY+a42PKcmZmpsTCW2Lt3b5WMz8vLC7Vq1UJeXp7si37P399fhzLOY0BAAD7//HMAZ8YtTHjywP7FF18oKUNbpb2HhobizjvvBOBsESorK6syH+Pp6YmQkBBs3LhRyS76xFtvvVWHFCYx6tatq7hxwYIFAOw4jAc8xmQrVqyQqCbHyraQDRs2KBnCw3CnTp1k39xDZ82apT2Ta/iSSy454yKIP0J0dDQefPBBWJalpAyTNDt27NDccB9LSUlRmwcTU5mZmXp2JqHKysp06KNQLufyqquuUsKZ6+Gmm26Sz+G69PHxUfsED/YHDhw447KMP4JlWfDx8cH+/fvPST7k5+cr/uDXrrnmGsXePADn5OSoJYbF0OzsbO17jMXZHrJlyxb9DhbSCgsL9V65Blq2bClZAs7frbfeqjah8qK4uBgZGRk4ceKEEpz9+/fXuHjQ37BhAwD7nTM2u/feewHYSTe2DD3yyCMA7P2Ov49JcyIgIEC2x3XYtGlTvQv+/s2bNysBxncxduxYxUnlQWFhIXbv3g1/f38lI7h3eXt745dffgHgbOW6/vrrleDivCUlJSmGYRxWUlKCpUuXAnD6IAok5+bmKnHIlraVK1dqn6IIcbdu3fRe6eN9fX3P8G9/BD8/P8TFxWHhwoXa46+88koAdoKVhVo+x6+//qq1xeTWtGnTtCcyBo+JiTknOcTEoI+Pj+Lsm2++GYA9Z/w+fVjfvn2V9GKyavXq1dqHuQ+5Axe0rcmyrIcsy0q0LOs3y7K2WpbVrYr+TiPLsm5w+f+JlmW9/l8+G2RZ1puWZe2yLOtXy7I2W5b1P5UKz/f3DAwMDAwMDAwMDAwMDAwMDFxxwZgzlmX1ADAEQGeHw1FoWVZtAD5V9OcaAbgBwIJyfPZdALsBNHc4HGWWZUUCmHz2hyzL8nI4HCWV+pTnASlZHTp0UHWQ9LpOnTqJKssK1L59+5RRPBslJSWi4ZHyCDgz/VVV8T1bkNfT01NVA9fr+Sioxmy8l5eXMrOsSh07dkxiUqzSsIrRu3dvfY3Z5fT09HPEf40IcOXiz9gos/CktbO9LSAgQNUAMjZSUlJUlXAHK4Hj8/b2VlWPlaXjx4+rssRrGKdNm6YqB5kH/F58fLxaglgFjYqKqhbja9SokSrarL4999xzqiiQhp2RkaGKDqtVrI6uXbtW7T833XQTALsq4c7xAc4xdu3aVTTl1NRUAMDLL7+sqg3bSOrXr6+KOKtHrNRu3LhR1/fSf02ZMkUUanfO4fjx4zUnMTExAOw2AlbTeL1m165dVXXkmiUdfsCAAaq6ccwxMTFVModeXl6IiIhA9+7dxdy5++67AdgVPK4Rspw2bNgg9sLtt98OwGZm0f7IPAgJCdHP0rewEr106VLtMVzHycnJVWajHh4e8Pf3R2BgoNoiyATx9vaWnXGfWrt2rdggXI+LFi3Sv/nMgYGB2hdZrWXVr169eqqI8t3Url27SsZ48uRJbN26FWPGjBH7x/WaZY5r3bp1AGzGACv4999/v56NewYZsYCTFUOGENkUycnJsk0y4VavXl0l4zt27BiWLl2Ka665RteXT58+HYDd5sM2iiuuuEJjYozC533wwQfx/vvvA3CyaVjtBpxinKxsr1u3TowNzm1gYGCV2ai/vz86dOiAwMBAPQPncNmyZRoj5yY4OFh7G+d38+bNmi8+e2xsrBhx/C/RvXt3+Rm2Jfz8889i7LC6X1RUpDaSkSNHArAr4hRtLQ9OnDiBH3/8Efn5+ejVqxcAp219+eWX8pVff/01ADtmJXOX6+27776THyVbyLIssQAY01Dk+KOPPtI653zFx8eLJfTggw8CsJktZL6RIRAbG6tYtjwoLS3F8ePH0a5dO7FUyOro0aOH2ll4DfGsWbPUVs29fPny5WoBff11u6bcr18/7QucD44lNzdXTEzXdhL6OL6XTZs26d3MmDEDgM3Wqaj4c3h4OEaNGoWFCxfKz1McduzYsXjmmWcAOBk7W7du1Rpjy26/fv3EZmFMNnnyZPkQsjLJ3vby8tJ+w7a1jIwMxQ6MaTt27KhYlvFraGio2vHKAzL0wsPD9YxkeERGRir2ZAvz119/rT2Bc5SYmCi2NluTXn/9dfleXg/PWKWsrEzPSx978OBBMbnmzZsHwPaxZ4smN23aVLFDeeDr64tmzZohMDBQLGye+WbPnq12RjJzfvrpJ7W8cS3eeeedYsySnVdWVqb5IKuLvqmsrEx/a9myZQBsn00bIVMqLCxM75ztcUOHDj1DTsNduJDMmWgA2Q6HoxAAHA5HtsPh2G9ZVrplWc+eZtJssiyrs2VZ35xmstwGAJaNmZZlJViWFW9Z1tjzfR3A/wPQ5/TvvPv01+pZlvW1ZVkplmU9d/rnmwLoCuBhh8NRdvq5Djscjn+d/n4/y7J+tCxrKYCk01/7/DS7JtGyLF2ZYFnWJMuyki3L2gCgV9W+SgMDAwMDAwMDAwMDAwMDg4sFF1JzZgWARy3LSgawEsAih8Pxw+nv7XU4HB0ty3oJwAewkxt+ABIAvAVgJICOADoAqA1go2VZawD0/C9ffxDAvQ6HYwhgtxmd/lwnAIUAdlqW9RqANgC2MTHzX9AZQFuHw8FGyckOh+OIZVn+p//ep7AZQE8A6AIgF8BqAFv+9JuCs9fNteeN2X3gzCv6ADtjyMzj2SgoKFBljVXwiIgI9cNWFc5mqpSVlalyyerX4sWL9Tl+raioSFUAwtfXV1lffp5Z0h07dqhiRf2aoUOHioFRHbKgFyP+jI1yXt9++20Azkz+xo0b9fnFixcDsDPZrLy4AxzX6NGjVUkgO2T37t0SK6Vw45AhQ5TxZ885KyxxcXGqplC3YNWqVaoauwMc32uvvabqAZlnrj3aZCDk5OSoSsw1RabQ1KlTVYlzZelVpApYFeAYx44dK80qiu3Fx8efoY0FAE888YTGy6oxq6t33XWXRBrZX5+bmyvGijvA8bECDUAMoZtvvlkMH1a/tm/frvfAqiH7rx966CGNi9XuoKCgKtkngoOD0a9fP2zYsEHXZpJV0a5dO/kAVmgHDhyo71P3JzY2Vj36FNsMCQnBkSNHADiroGSotG7dWmwE7iFbt27VnlHZ8PLyQmRkJA4ePCg/wOc9ceKEqtSssoeHh4sVQJ2WiRMnarz0j5s3bxYrlToWrBJu2LBB9kgWXMeOHavkGt/g4GAMGDAAu3btEjOLLKUNGzbIz1H3IT09XVVo2p5rhZ4aGGPHjtVYWb1mbJOQkKDPc136+vpWiY0GBASIhUXWI/27v7+/GDFkBWzZskVVbrJQOnToIH9I7YqgoCDtk2RkkAXcpUsXMRS4r/z222+y/cpGZmYm/vnPf2Lq1Kny6Yyrhg4dqv2Af79ly5ZiI9DOOnXqpDVE8d/jx4+rCk8xfTKH1qxZIwY318Mbb7whcW/+joYNG0priPO/YsUKibeXB35+fmjevDlSU1OlX0Vto2+++UbPxPe/b98+7WkUHHY4HPKjHEt+fr6q9nxfnPNatWqJTUE2u5+fnzTMuE9mZGSIAUdbLigoULzEiv/54OPjgwYNGiAjI0PMCtpq165dpcnBeGTs2LGaU+6DPXr0EEt0woQJAOx9kLbM38G90lVMlmv7k08+EduD+0urVq20jvlOmzRpUiG9EsDJuG/durXOD672RrslkzkuLk6MNO4fGzduFEOE+3r79u21V7KLgGs1KChIfverr74CANx22206P9FXHT16VO+He+aJEyfERHKNh883vqCgIGRnZ2tfIjukSZMm6jZgPPbYY4/J3zGeLi4u1t9yfb9krxNk9PXq1Uufp9h5bGysbJl+b+/evYodaA8vvfSS2FTl0WQ5efIktm3bhnbt2omhRJZVz549NS9c90ePHtVz8m9+9dVX+pu0gQYNGsin8qxHe6tVq5b2Dtr29ddfrzXKs6afn5+ErKdNsy9YWbJkifTB3IkLlpxxOBx5lmV1AdAHQH8AiyzLevD0t5ee/m88gCCHw3ECwAnLsgotywoD0BvAQofDUQogy7KsHwBcep6v/95p/DuHw5ELAJZlJQFoePYHLMt6CMBoAFEOh4Mc2w0uiRkA+JtlWSNO/7s+gOYA6gL43uFwHD79exYBaFGhF1RB0CjpXIDzLxSKljH4jo+Plzp2VcDhcJwj3FZWVnZOwsayLH2NAaenp6d+lg6/oKBANG1u+gxgs7OzFZzywDxnzhwF5UzcuP4Ng6rH79kobwOgg6WzHjFihJIXpGCeOnWqSm20vAgNDVWgzE138+bNCqhIse3bt682d4o6UsTMdRz8uZEjR0qM1J0IDAzUBsnNuaSkRAkbBtL33HOPRBVJc6eo7BdffCEKLQO86jJ/gB0wUfSRtGofHx8FMjwUX3rppTokMPBhUDp79mzccccdAJxU9qysrGoxxoSEBLUU8DaDxMRE0cDpR+vVqycfyABz5syZAGw/ykMK7fy7776rkvE5HA7tBwwSuWYAZ9KWwVZxcbHskYFddHS0Duqco4CAAI2PBwgGn7t379aewHGWlpYqUVzZ4C0VDodDexVbK8aPH6+DA/euwMDAMwTSAXuf5gGWVPIOHTpIqPWFF14A4LwJyMfHR+Pl4eu1115Ty0hloqysDAUFBWjfvr38Ad99y5YtFeDysBYaGqq2EIo/enp66lDFdVerVi0FzJx/BuvTpk1TKyL3kLi4ODz99NOVPr7CwkKkpKTA29tb/oF2WVJSIrFtPgfgPHhT0BJwtnlQiHv37t1K6PD2N67PPn366JDHBFZSUpIO3JWN2NhYPPvss5gzZ45aCJiALykpUdKNrXI7d+7U7S185qysLH2OSZy2bdvqcERb5k2NkydPVsxH+7300kslEswLBJjIA5zv+OTJkzqslgdBQUHo3bs33n//fbWm8QbBFi1a6LDGRGb37t11AKeg9Xvvvac2Eo45Ly9PX2Pig600ffr00X7KVs2OHTvqsM9bkk6dOqV1wHg3Ojpa76s88Pf3R7t27ZCbm6t1wUSWa8sI96uNGzfK9/HQumrVKiWP+RxXX321kkRM5jA5EBQUpHXO3+Hn56d3w7Wyb98+xUNMHBUUFGgdlBcHDx7EzJkzcc0118jO6E+9vLz0d3m2sSxLsQh9Ye/evVVg4xr99ddfJUzL9cpkWmBgIAYNGgQAElhPTU1VIoh27u/vrzMHbWngwIEVKgiXlpYiPz8fR48elZ1TJD02NlaFChZg1q5dK5tjEiU9PV3nIybsu3Tpon9zvdEud+/erfnl39y4caPiVa73vn37yiaYEFqzZo3stjzw9fVFo0aNULduXe21nKuQkBDZF+0mMDBQ/o6JtKSkJJ37GI/m5ubKvll04Rm4ffv2ig049pSUFCW9uOe+/vrrWpds9+7Ro4dsxJ24oLc1nU6ifA/ge8uy4gFMOP0t0iTKXP7N/6+sZ3T9vaWnf28SgA6WZXk4HI4yh8PxNICnLcvKc/msTvaWZfUDcDmAHg6H46RlWd/DZvgYGBgYGBgYGBgYGBgYGBgY/ClcSEHgOABlDocj5fSXOgLYA6BdOX78RwBTLcuaAyACwGUA7oP9/L/39RgAwX/0Sx0OR6plWZsAPGVZ1iMOh6PUsiw/ANZ/+ZFQAEdPJ2ZaAuh++uu/AHjFsqxasFk7owFsK8e4LggiIyPxxhtvAHBmxmfMmKGMZVWB2XVWNV1ZMme3E7jC4XCcc+21h4eHssCsXrl+hvRXCnuOHj1a4k+un+P4jUiwe8AKBa+mJEU5KipKlXG2JXTo0KHKbbQ8iImJUfWTGfdWrVqp2kbaZ1lZmcbDLD+vMmzUqJGqDWQiHDp0qFqMz7Is0UJZhXr44YclfslqS3p6uqpPrLqwGtmlSxf9LP97+PDhajE+wBa5JKOEVaFvvvlG1RoKM+bk5MhHsIrGSmb37t3FuuFnBg4cqOtK3Yljx46pIsv5cqVU03decsklqgyzLeGpp54CYDMQXCuHgG3HVTGHRUVFSE9PR2FhoZhkL7/8MgB7rliNZCtAUVGRBKo5Fk9PT+0jvPryndC2ZtgAACAASURBVHfeUVsaGTYUgOzSpYu+x3aCffv2VZmNZmdnY86cOejQoYNYLGzhKi4uFhuPDIy0tDRVP2mrWVlZsk0yEY8fPy7G2qOPPgrAKSDcvHlzVQdZ6ezWrZuEVSsT+fn5WLduHdLT0yXKSWbMiRMnNAZWYw8ePHhGLADYwpBkatBv/Pjjj2Jx0N9yb/jhhx/kb8kKa9GiRZXMYUhICK644grUrVtXf5PMuYEDB8pGyUry8/MTS5fzmJKSIl/JMZeWlqqNgr+DzKeYmBhV5ckk7d27t1pyKhslJSU4cuQI1q1bp6u0Bw8eDMCOocikpMj73r171drDvW7hwoWyUbZRREdHqz3L9SpiwBag5TshKywhIeEcVtUnn3yidkWO38/PT2Ki5UFWVhaef/55PPzww/KLZEukpqaq3Y/+Y/369WJ7MDYcPHiw4hW27V1yySViBP/73/8G4PSna9eu/f/sXXd4VVX2XTekd0ICoYfQSUBQQKqEgKAoVQVEEDu20Rlx7DqIogz+ZgQFrKgjiAVHUQQFxdC7AtJLILQAKaSRXt7vj5u13gMcSRzCC85Z38eX8HLfe/fcs88+5+y99jryvRS+XrJkiZ4rfWu/fv3EXqTYaWlpqRgbFYGHhwd8fX1Rr1493Te/57XXXtNaivdfXFwsRhOZlm3bthUTg6LF/v7+ah9Z97fccgsAu3/IhqP48LRp08SO4X3cfvvtYkbx+hUrVsjfVxQBAQG48sorUVBQIMYMv6OgoEDjiuuxBg0ayM+zvKywsFCMIrLx0tPTVR7FZ0E/0rJlS/UJ7TYwMFD9z59r1qyRWDU/99ixYxr/FYFlWfDy8kJ8fLzWF/Tdvr6+KqXnmqOkpERzGsfg0KFDxbjjWi4oKEgMIj4brr8jIyP1HZx/WrZsqb4hq/PYsWMSCOe6dfTo0WKgVgTFxcVITk7GxIkTxYgkw6Vly5YSrSYrateuXWLZcXxcffXVYiZxXJ4+fVr2xz0hGS8pKSli5dP/HDhwQPbD69u3by92G++tadOmmDJlSoXbV1W4mMyZQACvl5cplQDYD+Ae2Cc4nQ9fAugKO+DhAPCYw+E4YVnWf3o9HUCpZVlbYWvY/NbMfReAVwDsL39fPoDH/sO13wG417KsXQD2AFgHAA6H47hlWRMArAWQCWBLBdpkYGBgYGBgYGBgYGBgYGBgcFE1Z36CLeB7NqJcrvkAdjCF/49yue6v5f9cP9PxH14vBhB/1ve4fu71Lr9nAxj3H+55GewyLP6/EMC1/+Ha9wG8/2t/czcefPBBZa8YGWb29GLgt9gpDofjHBaNh4eHosRk33h6eqpOkZlvIi8vT5FhRkYXLlyoaD3h6emp6wzcA4pgsh6UEfKAgABlIJllJuvE3cjPz1f0ndoH//73v5Vto62mpqaqdptZOmZ4Ro4cqYw/MxytWrWqlLhhVcHPz09aGMxePPnkk2ozj2acN2+eMqTM3FNMNjw8XAKKzPhWJntU1UhPT1e2hv6oWbNm6k9m+a+44gplU5lFZC1yVlaWxP9Yp83MpLsRHBwsO6RNDR8+XJlv2l5xcbGYFcw6UWivtLRU+knM6LNm/0KDeiX+/v7SK+G9Pv7449I2YlYyMjJS2S5mQ8ePH6+MOrOcderUkd2S7cC579///rfYKvyuqjz+PCAgAJ07d4anp6f8HPvo559/ls1RCNHHx0dClsSVV16pZ0EdgvXr14vxxYwvbXr//v26jmw/zpsXGhEREbjnnnsQGRkppgB92+DBgzVG6FMcDoeywNSvCAoKElOCmjqtWrVSBpTrFM75MTExel5kn9C2LzQKCwuRlJSEtLQ0jXtmkQGnj3dlApDN46rdxNf4bJo1aya/QbFRPpdly5aJ2cHjppmRrwpQjPQvf/mLxhD9ZO/evWWPZNS1b99e2Xja3Lhx4/Re+tPQ0FCxnzjXsx1169ZVVps6KSEhIdLYoc8ZM2aMWABcBw4cOFBHVpNJ91uoVasWbr31ViQnJ4txwzFft25dsba41mjRooXYC9S2OX78+DmCuT/88IPG8oQJEwA41y1dunQR44f33bBhQ2npEC1bttT6gAyx0aNH61j6iiA/Px/btm1DcXGx/Dmf6c033ywhY85lH3zwgfwBGZ+uwtdse/PmzdVftFVXMXXO7ZxrmjdvLqY6WTqDBw+W9goPBoiOjq40W720tBSZmZlYvHix2F3U9/n+++/FGH3qqacAAEOGDJGP4BH03377reYB9uUNN9wgDUTO8e+//77aw/mG4zciIkKsI66D6tevL+FmHu5w6tQp+aaKoKioCAcPHkRubq5YLOyHffv2iV3Gfti+fbuYhLTpOXPmiO3C579v3z7ZJPdCbEt0dLQYXLSH5OTkc45jb9y4sfSKuLeKjo6WTlhFEBgYiO7du8PhcIidQn/i4+Oj76TvuP322zV3kYGemZmp8c7xERAQoPfw2fP/6enpmn/osw8fPqzxO3nyZAC23+X8RObm0aNHZV/0de6AZco6Li4sy7poD5xOKyEhQc6fAktc4FYSPzkcjo6/dYFr+34t6PJr9nY21dnDw0O/u4oEExTxpPP09PSU06FjclU+P/vzAefC4iyB4PO2L8S7tqNb+PDfuuQMpL933uo6oW+9ik/KADD/456Vun7X5EfO2z7gf8tGqxp/tPaxLIMLt4ceegiAfdIPN1mcPAcMGICRI0dW9ivcbqN33303AEjQlIvwTZs24ZFHHgEAbRBuvvlmLQorgQvehzNnzlTAgaUVI0eOVEkWAxarV69WYI2LVG4MDx48qN8pgjh58uRK09BRBe276667REvnht3Pz08Lbm4SmjRpooU5gz58Bnl5eSo94HwRGxuLe+65pzK3AlSRjQ4ZMkTBXW7uU1NTVZbL+W737t3aOHIxz8VpVlaWyk4oiBwZGXnGaV4VxAXvw7feekvzLTf9mZmZWgCznCc8PFyBjCFDhug1wC47oX9hUKZXr15V0oeVbd99992ncgqKyfr5+WkTxM3TqlWrFJzg5oMbJC8vLwWpWDqRmZkpmj03k/7+/trsM+AYHR2tzdrrr79eJTY6bNgwzWncwCxevFgbSM5p8fHxOqGHfoblIcuXL9f1DECVlJRoE8VEwe7du+V7OXeeOHFCQYIPP/zwgvfh1KlTFfhkSYVlWXrGbJOvr698DgMA3FD//PPPCnawj1JSUtCnTx8AzlKugwcP6uQoiqFv2rRJJYqPPHL+9Vpl2/fiiy9q3HCzHRgYqDHFueH06dO6jsFClolGR0cr6MM2x8TEyPYZtHzvvfeUCOAGPDAwUL7qpZdeqhIbjY+P10laXJOMGTNG45DBq379+qmsiQFwtr927doSLuYJbfS/gNOm/fz8NL9wf9G3b1+V47711lvnbWPDhg0djzzyCEpLS/UdDBR06dJFgWl+Zps2bTS3MRixdetWJTKYHGvSpIn2QywJom2XlpZK9J82nZeXpyA0A46bNm3S93NtcPjwYT3fqVOnnrd9jRs3djz55JPIyso658Rdf39/7edYalxSUqLxztKrmjVrKqjKucNV3JrzOq/fvn277pc+trS0VL6Dfic2NlY+hknI+fPnKznw8MMPV8hGqwIe57/EwMDAwMDAwMDAwMDAwMDAwKCqcFFPazK4uOAZ8V5eXqKqXkyaFiPJriyVXxMCPvu6XxP69fLy0u+MKrt+LrO7jLiSLuqKsrKyMxg4Bu6Hu220qvFHax8zFMyA8yjYsLAwsWhY7kVK+qUGZp7INmAmOzo6WgLITz/9NABn9tHdiIqKEouC2cLZs2ejffv2AJylPZdffrn8LfuS5QfJycnym48//jgAVNkx05VFdHT0ObYXFBSkNjPbnJiYqHIm+noet/nGG2+ov1j643oMcnUAM4sUbjx69KiYMC+//DIA26ew3a7lMIDN3iBziM+kOpROAvZ9kK7Oo6Q9PDzUP5zD586dKyFVMmdYglFcXKzSbH6Wa0bbnahdu7aywLTVgIAArVfoVxo2bCgbZtkHWQrXXnutmAd8rXv37iq1JJvvm2++EROFjKOtW7cqo15VCAwMFGuA4ysoKEgZd2amv/rqK5VLLliwAICzzHXPnj0qt2BpX0lJiZ4TS0cCAgLE0OB3ZWRkiIFUFTh48KB8A59//fr1xQqZM2cOAOCJJ55QG8iO4TXjx49XCQaZC66iwixxvPbaayW2SzZgSUmJRJWrAt7e3rIbsu/8/PzUb2y7q9Auy2k4PgMDA8WcINMvMjJSbDFXEW+yM8h22LNnj5hGFBG/0LjmmmvOYUgkJydrPTJ8uM18T0pK0v2RCUs/SkYNALzwwgsAgP79++s69ld2drbElDk2v/zyS81BFQGF1Xv27KnvJQPu/fff17PjWCkrK1MpDpkzp0+f1n6IfvHTTz8V6+dsloy/v7+YQezf0aNHy5eSJZWSkqLvvf/++wHYJWCVKVmnIPD69et1YAuZucnJyfKLZJQ3btxY98TyJ1c2Jf3/X/7yF61rnnnmGQD2/AfYfUEboD9NSEjQuo3M2c6dO6tsi+PybDkMd8EwZwwMDAwMDAwMDAwMDAwMDAzcCMOc+QOCdZ48Uq+oqEhHbl5MQVxmh1jT92v4NX0Z1+uZ6bQs64wjVF0RGBioqDEjynyP62f4+PiIlcMa2LKyMnOcthtQXWy0qvBHbR8zJhTOYwZ8/vz5mDZtGgBn5oYZ0EsN1Lcgw8mVXTFunK0d71p/Xx2wePFi6VhQqyIwMFC10/Spx48f1zGYFOCcOXMmADsDTz0EHmvLzJW7ceDAATFBaIMfffSRMtT04WlpacqYcT4hmyAsLExChmQ2MBNaHdC7d29ls12FQnnMMo+y9ff3l+4H+5rsk1mzZimjTzs+W0DfXWjatKkys9R12rp1qwRg2ZbS0lIJDFOng5lkX19f6XnQfqtKELiyqF+/vgQvaZceHh5iN1H3qV69etJXIAuFc8KePXs0PsmOWrFiha7nc3nrrbd0BC1FoVetWlXlPteyLKxbtw6ArTMC2MwZalqQ1fLNN9/Id3DNRR/UuXNnjWXquuTl5ck2mOlu0KCBbIKsGh8fnyplBwUHB0u8lvocRUVFEn3lWjIjI0OMGc57PG48LS1NR0j/9a/2WSURERHSBKMGxrvvviuWDBlCK1eulGhpVaBPnz7SBeLPwMDAM5gtgO07aa+czykW37VrV/Ut/WheXp5YYLTf8PBwCedSL2Xbtm2oW7dulbUPsO2Nz/jaa+3zW1JTU6VlxDng6quvls7RK6+8AsDJNkxPTxfrlFpnvr6+Ojad801hYaHew9fatWtXqXWBt7c3GjVqhO+//14MFzKYunTpojl83rx5AOx5jH6DPt6yLDHouO95+umnxXAim4n3tWLFCjFRKIq9fPly2TznnzvvvFPMIbJSQkNDxc6saPsaNmyIvXv3SsuG7JcjR47o+VHXKSkpSf6E2obJycli+FLrasSIEdKJoa/kz6ysLPkJPqtOnTrpvikAvXr1avUzfU1GRka1YJsa5oyBgYGBgYGBgYGBgYGBgYGBG2GYM39AsF6bNZDfffedW3UuGFH28PBQNtNVe+bszIrrexgNdj1y+9c+g9k3ZqAsy9JnsL6wtLRU73HVtTG4+KhuNnqh8UdtH7UweDQns7f3338/lixZAsCZyWa291LDgAEDAJyrBxEZGaksNbUleCSwuxEVFaWMM+uwu3fvLpYhf7Zu3VpH1jM7x9MzVqxYgX/9618AnNmpqtawqCg6deqkY7OZ4YqMjFSNPk9tyMjIUPuef/55AE4GV3JysjQSyL4hA6o6IDg4WKfysIY+NDRUGXpmG0tKSnQ8MZ8F+/D6669X5p/+htlVd2Pq1KnSzeE9NmzYUKdwsNb/yiuv1KkpnP+Z5bcsS9lfshNps+7G7t27ceONNwJwaiq0aNFCbaEmyaRJk2SbXLe4Hp9MfRnacW5urjK91JNq166d2Bns5y+//FLZ5arC9ddfrwz+O++8AwC44447lH0mo6J27drym2QRMRs/efJktZ+MlGeeeUaMN75v8+bNsnPqnTzyyCOaZ6oCPXr0EDOIp/scPHhQjDbqtQQEBIglxCOkubYsLCzUUcp8LSQkRNfzNL3GjRuLRUK2W1JSUpUyubdv3655gqyEq666ShoqZGn4+/uLGUWmCf1IYWGhGBmca+rWrSvWAhlQ+/bt09gmE8GyLOmJVBVatmwpv06/uHPnTj132ui2bdukF3PHHXcAcLLb9uzZo/dyv3Hs2DE9O7L2LMsSm4onV4WHh4tBVxHk5+djx44daN26teyL+oTe3t5iLpExeOjQIbF56ReOHz8utgnvcfPmzdrn8DNoW97e3poz+TxCQkIwdepUAMDQoUMB2KfDkTnE8ZuRkSEmTkVw+vRprFq1CoWFhfKFnLf8/f3FnuN6a/PmzfL7tKWMjAyNJba9T58+Yv/QF9I39O7dW/o6POFuwoQJOsGQfVqrVi2Nd/qYiIgI+WV3wgRn/kCg+Oizzz4LwGnsLD1wN1wDLIRrWREdiWVZ55QkFRQUnHH8GuAsTcrMzKzQhOZwOBS8YSDorKO0DaoY1d1G/1v80dvH8UPhPG6UEhMTtfHlQowT8KWGhQsXAnDSfbnxSE1NVTkXS0Wuu+46LV7dCS8vL20guGDx8/MTZZiL1fXr10vYjwKOXLTWqVNH7WIghAEBd8NVCJb05z59+mgj+/HHHwOwKfdcdF5//fUAnOVAISEhmldox3xW1QG5ubkSnOaidNmyZTpmmZu7wsJC0d8ZLOXCOSEhQRt4VzHF6oCBAwdi9OjRAJxlkTt37lTpA0s7UlNTdTwty0K5oYuLi1Ng1PU4VG4C3QkfHx8djczSlMTERAVPeL+XXXaZNgzsU/rOQ4cOifrPjVpYWJg2C9wUffTRR/JTDFLddNNNKjNhGcmFRnp6uu6BpUYlJSUKCnE8btq0SZs6jjEGiB9//HH9jT6rdevWEl6nuHCDBg3wwAMPAABef/11AHYJEYPKVYEff/xR60sGToqKilT+QnHj77//XvfBTTFFYL28vDQHctOflpamTSiPbe7Zs6c2wXyta9eu2iBXBTw9PXH06FH9zu+mWDF9a2pqqiQCWBrE+QKA/BTFVI8fP64+5fi8//77FaS46qqrANhzDAM7VQVPT08FnrgZDwgIUECepbu//PKLgn8MFvJnTEyM9ggUrG3Tpo1snzYaERGhcU1x540bN+oZVwR+fn5o06YN8vLyFCRjQHPbtm0KgHGv4nA45P8ZTJo+fbpKrvmsFy1adIbYP+A8Kvyzzz7DX/7yFwCQ7/Ty8tJnfPLJJwDOnHe5F/u1w1Z+C97e3mjSpAlyc3PV91xzNG3aVELAfO2mm25SkIU2dfLkSf2dZUoPPPCAjqBnKR3H2FVXXaVgIeeXfv36SWCeQeHs7Gw93/j4eADAo48+qvX6e++9V6m2XkiYsiYDAwMDAwMDAwMDAwMDAwMDN+LSTG0anIOwsDC89tprAJwZCkYHSdtyNxwOh6LRZLq4Ml74Ny8vLwl6kers7e2tvzMCzizo6dOnf1MUkO9zZe0YxszFx6Vgo/8N/hfax6wYBUhJMU1NTdX4YlaHdO9LCWFhYSpdIgOBmZqgoCBlckiZZcbR3QgPD1fmmVn2lJQUZfXIcOrVq5eYFPStZNW8+uqryoYzQ8zyJnfjq6++EivNVbCZ1Hva3Lp160T/Zv/RRsPCwtQ+9jHZGtUB06dP1zGzzKgnJSWpNJJZwejoaGVmmTl98sknAdiZTvY1s/csi3I3OnTogDvvvBOAM+MeHBwskVvO67Vq1VJ2m6wLlqYtXbpU2X0ybVmK4W54e3tL9J3laXFxcWKzkP0THR2t9nF+oB85cOCAxi/Lapo2bapnQ8ZiUFCQWDdkCUyfPr1Kj2EG7JIJjq+BAwcCsJkCFGymHW7dulXjjqWHZDHs3r1bfcZxeOjQITEPWEr59ttvi/3F43xDQkJUpl4V2Llzp8bPZ599BsB+1mQ0sPwoKChIpWi8HwoDv/zyyyrLIAsnKSlJGXwyUebNmyfGzG233QYAWLNmjY4/JjPqQuLEiRN6vmRE1KpVS3bDNt9www2yTdqeK4OUZYZk9X3yySdiepFxun37djHlKCbcpk2bKi8l/fzzz/XcWarSp08flWBzDeN6yAgZTrNnzwZg9xEZM7y+tLRUTC8+r759++oIZjJoY2NjxdAgA/W3UKNGDdSsWROHDh3SuorzUpMmTTBr1iwATnHp2rVr60h3jo8bb7xReybao6+vrxgonEPoT8eOHSv2KPdCMTExYtGQkXnDDTeIMUMf1aFDB/z444/nbdfZ8PLy0rigD9mxY4fKwXhvJ0+elFA/78OVDcX7Wbt2rXwG58E///nPAGzbpvgvWXfbtm3T8eH0TceOHZN/JqN44sSJ1WJdYJgzBgYGBgYGBgYGBgYGBgYGBm6EYc5c4iArZPHixaqBZcSQooLVCWeL+Xp4eIhlwHpOb29vZTh5nWVZqj9nVpeRYtejQl2P2SYrx7Bk3ItLzUYri/+l9jELwawL64/79esnEWQK6TGTdinAtY0Uc2TWhhomCxculL4AMz+Vrb+uKoSEhMgfUh8hKChImUFmAaOjo1WLzVprshm+//579OvXD4CTdVFdWAkOh+Ocev/69esrw8VjxOvVq6f+o+gss4b169fXsdRkGZGFUR0QHx+vfnJljFIvgPoILVu2lOgjs/Ds00OHDqk2n/3L/7sbK1euVPaTmeoNGzYoI8+2e3l5iWlI9gBZUyEhIWdo0wDVh90VHBwshgV1jpKSkvDqq68CcB497evrq0wvM/FkIKxevRr33nsvACgjf/jwYWlFsE9r1aol0VBqo4waNUpMuapCUlKShHvJ3PHz8xOzicyZ5s2bixXDzDjXdH369NHzIYti8eLFagc1V3r27CnmARmLoaGh0hqsCtx11124/PLLATh1VWrUqCHtOLJ/AgMD5Tu4/qROR2lp6TniuJmZmWLdkJ1QVFQkoVYyNrp27VqlOl+JiYnyLXyOvXv3FuuDzL0HH3wQ/fv3BwD50+HDhwMAZs6cKTYUGVNXXHEFevToAQDysbGxsXov7fuzzz6TDhqf14VGgwYN1F9kkaSkpEjXiTYXFBQk3RXq61BgNz8/H4MGDQLgZFkcOXJE/cn1wsSJE3HPPfcAAD799FMAts/m51UEXl5eqF+/Pnx8fOTv6NPWrVunPvn73/8OwO4jtoXIzc2VX3StMOjZsycA55Hnc+fOBWCPLepekTGSlZWl8cYx4O3tLQYq55HU1NRK6Zh5eXmhTp06OHLkiFicXEe1a9dOTBi2aeXKlWoD/V1QUJC0u7p16wbAFlan1gxBJtHgwYPli6hHN2TIEM0rHLONGzfW2oH6VjNmzNCBF+6ECc5c4uAGiJsFwFa0B2yKbHWC6wlKFCOrUaOGBiLpgw0aNJBTJw3u8ccf16TNzRIXIq6nPLmWLp0tPux6QtPvVcQvDfRBZs+oCl8/q03Fxd2eP3p9pe6l4beVU73fVamrK4758+drg8cF6EMPPaQ+5EJlzJgx2hRVVxv9NUyZMkW2xEVJQkKCJgnShKdMmaIJ+lJq39tvv60+4gRco0YNidJRvPOjjz7CBx98AMApWMdA6SuvvKLPq+oNwu/B1KlTRUNn8OjYsWPyJRSlu+uuu7TI5OaCi7krrrhCi0xScO+8804FpdyJl156SQK43LS2a9dOp0lx4bFgwQItqM8OdnTq1EmCnpxPzvah7kLr1q21MObCOyoqSvRybmQbN24sGj7FDV988UUAdnvZt1xwcjxXB3h6empDxE3bunXrtJkiRf/IkSPa1LO8ie3v1KmTbJM2zVIYd6NJkyYKzjz11FMAgCeeeELjksmXAwcOaEM0fvx4AM7N08GDB7UxOLs8w92YOnWqNkN9+vQBYCeG/vSnPwFwBmJq1KihMUcRWZY8+fn5aW3C53H48GH87W9/u0it+G3Url37nDVcSEiIRIm5kWzbtq3KfBiU4caoQ4cOKp+hCOeCBQt0Sh7BZwTgd5/Q1LhxYzz77LPw9PRUSQOTde3atcPy5csB2OsVAHjuuee0QeeJPAMGDFAZD08Gi4+PP6d8kL5k9erVKreg3fbr109zpit4IhfB+bWiaNKkCSZOnIg1a9aoXRxPLVq0UACN6+ioqCiNF5bmpqamqoyE/n/cuHF6/pzbP/zwQwB2wImbbI7FunXrag14odGgQQOMHz8eSUlJKldyXVMysMTS1l27dqkMjuuZRo0aSZyYQbXk5GQFvjln0qYTExNlr5zza9euLYFd13Jhim+zdOvNN9+s1EmVpaWlyMrKQuvWrXXfTKp0795dIrq0x+zsbH0+EzFlZWUK+LIcsnnz5uonBj5pjzVr1lTwkXazevVq+Vu+tn37dp1oxbXAwIEDNQdXBIGBgejWrRvmzp2rIBH3CRs2bFDglWvlI0eO6JnTx+/atUvrNgZIhw4dKlum3yUSExPVt9yHjB8/Xs+BfreoqEh+mQnV9u3bV+mJaRWFKWsyMDAwMDAwMDAwMDAwMDAwcCMMc+YSBSOQzJgBTsEoZrOrGyzLOud4XdcIJf8WGhqqTBLLRJ5//nmJYJEm6wqyZ1w/n6UXrkd08/sYBa4OEdJLHfv37xcNkNntw4cPiwnFjEa3bt2qvY3+GizLElWXpSP33nuvMtSMuDdp0uSSbN+WLVuUlSfFdNCgQXjrrbcAQH2bm5t7SbYPsG2QfoBZqaCgIGU76Ufr1aunrFBsbCwAJzV6wIABynTStpmNczeee+459SHpyosWLcLkyZMBOMWZt23bhnfeeQeAk5VAxk2dOnVUskBGBlkb7kbXrl1VMsLs/HfffaestWvJAGnzzM5zHvjb3/6msUqQ8lwdYFmWMoW0ua5dnVFXDAAAIABJREFUu4qCvXHjRgBAjx49NG+RGcVM6pYtW8S44N+qS1mvw+EQhZx+9IsvvpCNMbt//Phx0epph8yu7t27V6XLzDL/2nrAHfjss8909Cp9SIsWLSQoTir/ihUrcN999wFwruPoax966CE9BzLbWJ5XHeDp6akSM2bUMzIyJBDK8qu//vWvYsVwTcZseVZWlsRxae9du3b9XSKj54Ovry+aN2+OU6dOyebINv/222/FPCCDpk2bNmJTskxk69atmh/IFMnKypLALBk0zMC3b99edk7briqB0aysLCxatAj9+/c/58hjT09PMQXpC1atWqV5jXNfQUGB2Cd8NgcPHhQDIy0tDcCZTDW2i0yE+Pj4KmN35eXlYePGjejYsaPuj6yeJUuWiAFCMe6HHnpIotps97x589Q/HFeuLEOyZMmyuO666/ReshhzcnI0X1Act1OnTvI/ZPPs27dPc1BFYFkWvLy8sHTpUq2fOVa2bdsm+6Ktbtu2TQxErmmCgoI0Z7BN69atE3uStnH33XfrGpYsszyyWbNmmDdvHgAn+8bX11fsK9pwSEgIunfvXuH2HT58GA8++CCGDRum9tF+6tSpo3mNe7hevXqJ2ULf17lzZ5U18bsPHDig0jz2AZlCUVFR+g7aTP/+/eWXWS41ceJEla9xvTBw4EC1350wzBkDAwMDAwMDAwMDAwMDAwMDN8IwZy5RsJ6emReg+tSWE65aMPw/GSvMPJeUlCizx/rQgwcPKiLMIyc7deqkyD2j265HZDMaziypl5eXMmz8rOqSQfyjwd/fX9kF1oY3a9ZMmUIKPi5fvrxa6pGcD/7+/hIaY71yfHy8hNTI7jpx4kS1Oba2MqhTp46ylhS3W7FihbRJeLzj5s2bq52PqShmzpyprCePLc3Pz5eeDLNTa9eulfAhjy5mluXLL78Ue4PZ1eqiybJ+/XrVzV911VUAbD95tjZAjx498NJLLwFw1tIzS7dp0ya1i2LI1QVeXl7KCJJN0aZNG2XMmBls2rSptALITiAb6Pnnn1f2kVnGDh06nKP74C706NFD2T5mhjt27Kj74zGhrsePklXDrG3fvn2V4WRmlAwTd+PIkSPK6tNGN27cKEYDtUnCwsKkIUDhY/ZXSEiINDVc1z7VAevWrTtnrZGQkKDnzyz2iRMn8MUXXwBwMoKosdO4cWPNNdSyoD5SdYCvr6/GGo97/+CDD6TZQgbXsGHDJGBN9gZttWnTpvIvzMqzny800tPTMXv2bNx+++161mTP9ezZ8xzWVVxcnMYb+2HBggXSkCHDpkWLFmLT0L9Q62rPnj3Sx+CalsyMC43S0lLk5ORg7dq10gmjrki7du3w/PPPA7CPTgbs9TFtjwyasLAw9c2WLVsA2OOMDEX+JAPh2muv1Vqcz6oqmQalpaU4ffo0li1bpnnMVSCaGiS8vzlz5mgeo8D9tm3bZGPsr8DAQPlb/iTjJTExUew92nFYWJh8Dvs3MTFR4r1kot51112V0kgqLi7GsWPH4OvrKxvifqZLly4SSnetLKDeIefEn376SffrqoFEnSXqBXF9V79+fT1LjoERI0ZofUv//Oyzz2pdQQZ8SkpKpXS+wsLCcMsttyA4OPicY9S9vb0las/5KiMjQwdPcO8QExOjeZ+Mpi5dukgQmH1POy4pKRFLh3pYOTk5YumQ9Tdr1iz5Z/qi2NjYasHGNMGZSxA9evSQyNylAG5gXAV5udiyLEuOnQuakpISOSfS7KKjozXw6JBI7fbw8DinrKmkpETfyyCNZVn6DlPOdOHQqlUrqcy7nj7BRTdp9llZWZdkcOarr75S6Qc3TU899ZSE2rhZGDhw4CUZnHE4HFLKp+Cew+HQopoL0j59+vxuYUZ3o1+/fhLMY9AlLy9Pm0XSXePj41VSycU5qbNr164VVZ/+g0KR7saCBQu0UGEAolmzZgossazJ09NTpWlcuHNjHx4ermfD+eXYsWOYPn36RWrFf0aNGjXOEHIG7M05Syx4jxEREaK0sy0M2A8YMEDBNdo7F+rVAUeOHNHcxnKBXbt2qY0U5oyNjdVCkv3LBeb69eu1GOXfXIVV3YmHH35YJ46wfW3btlV/cYM/e/ZsLdQ5XzCQmJ6ernUAN8d8Pu7Ghx9+qFOouCm6/vrrFQxg6VLnzp1lw9xwcINy6NAhbSK56atOaxXLsuTz+LNbt27auLEExs/PTwk4+lHa7KRJk1SawBJL+uELDW9vb0RFReHbb7+V2C2D86mpqbIr2qOrn2HZj8PhULu4ec/IyFDAg+U03Ow++OCDSmwwccP17oVGUFAQ4uLi0KFDB50C9cQTTwCwbYklvIsWLdL90C8wmDBnzhyVxNJ3pKWlqdzlzTffBOAs4U1MTNSGnf24YcOGKmkfYAe9+vTpg40bN2oscY6LiIiQHdJnbNmyRcEZzusHDx5U8IolQYcOHVJgh/3PNd3Ro0cVFGUbly1bpqAQ58zDhw8roMKTn/bv31+ppI2vry9atWqFzz//XKeY8f15eXnyjxSUXrVqFaZMmQLAeRLhli1bVNo7cuRIAHbyic+GYs2819zcXJ2WxUBPQECA5kd+PksCXZ9lSUmJxKQrgoCAAHTs2BHPPfecfJrrqXNM+PGZxsTE4Oqrrwbg3BOmpqaqXbTRpUuXai6gj+RaLScnR36H35WWlqYkMv3V8ePHJZdB+1m9enW18LmmrMnAwMDAwMDAwMDAwMDAwMDAjTDMmUsQPXv2POcI0MTEREURqwsY9WQU+NdKjQCnECCvz8/P19+Z2XCl0jEjQ9paSUnJGaK/gM2mIU3aNQpaHSKifzS0aNFCkXTSJ3v27KmsJ6PVQUFB1c5GK4L4+HixY0ifbNGihTLStLOcnJxLsn2FhYW679mzZwOwxxHZQmRATZ48+ZJsH2BnikhpZRlPgwYNlCG69dZbAdjZXWaPhgwZAsCZIU5LS1OpHunqzPa4GzfffLNYg9999x0Am4lB4U1Ssz08PHQ0OrPAZJbs3r1b2bHXXnsNgPPISXfjl19+kbAqs28zZ87UsbfMKm7fvl1tZpaQGbcDBw4o281nxONJqwOOHTumTC5FCrdt26b57plnngFgs/aY1SXzgoy20tJSZV8poBgfH49//vOfF6kV/xm//PKLGLHEyZMnVab8xhtvALCZFmTFsBSIWekxY8aIoUCWX3URdY6Pj5cgMIU3vby8dKw2WXeTJk06h5VI0fWQkBCtechmaNSoUbU5SrtGjRoSx2dZ71NPPYWBAwcCcNrjwoULMWbMGABOBgr7raysTMfmkjFEpsmFRmRkJB577DFMmTIFw4YNAwAxKH19fcVgIrMlNzdX/oPsp5CQEP2dDJ/U1FS1a9KkSQCcPiU7O1t9TqYHGRcXGpZlwdfXF3v27NEY4TH1wcHBKvEkuywsLExSARxvqamp8qlkYkyYMEGlhVzTkZnp4+Mjv8lyLQoKVwXy8/Oxbds29OrVS/MY11ybNm3S8dZk+vTs2VMMRP6sWbOm9ghkx9SpU0clzJzz7733XgB2uQvHJp/hXXfdpTIuflb37t3FxKFNZ2ZmSoifjPLzweFwYPTo0boPzrtbtmxRSR3HVp8+fWTDfC08PFz9Qxt95ZVXNL64fuUeq7i4WD6IpUYnT54UA5NzJ58zcKYg9Nn7z9/C6dOnsXbtWvTq1QspKSkAcIaoO8cly/FOnDihv5OJv3v3btkmGWJxcXFn9C/gPBLd399fe0EyaP38/HS8Nlk1+/bt0z1xTt27d6+OlncnDHPGwMDAwMDAwMDAwMDAwMDAwI0wzJlLHMw4xMfHV0qk6WKCbJXS0tJzNF/KysrOiM4S1JBh1rC4uFhMGVfWDXCmlgzhqjlj2DJVi5UrV6pu9OWXXwZgZ2137tx5xnUOh0MR/EsJPj4+Es+jOFt6erqy9cyw7d2795Js3xVXXCEhQGb6xo0bJ/0ctql+/frKTl1q2Lx5s0TuWJccHh6u4xOZ9Vq5cqX8DI9KpV8dMGCAjtB0FRKsTP11VeH48ePqQ2ZOFy1apPpsZoyGDRumrBdr2ZktO3nypGrvKdjZv3//i9SC34aPj48ypNRR+Mc//iH2GnU6Nm/ejMceewwAlOFn1vvo0aPqK/ZpVWolVBZ79uxRhp3ZzPvuu09zITUCRo8eLZ0LHgzAPmzdurWyiXyturDdcnNzpTtCNqWHh4f6laK4oaGhYrnRDjl28/PzNa+TqfD888/r2bgTOTk5yrzz/tu3b6/fOd4mTJggQUpeT8bi1VdfjQULFgBwaplUJ522Bg0a6LhsiqNOmjRJrD2yDG+77TbpOdG3kmEyffp0sZ7IFGMm+0Lj+PHjeOmll5CamiqmINkGP/30k7RLyKZcuXKlMulsU2BgoPqAuiX+/v7yIWQUcLxFRUWJYcH3UfPlQiM/Px9btmxBTk6O2C/07+3atdNcRh8QGBgobQ0++5iYGLHXyMCLjo4Wk4FsYTK51q9fr+dAceGOHTtK1+xCo2bNmrjxxhuxbNky+XIyK1wF08k28fb21tqFDJNHHnlE7EE+p9zcXLELyRQh0+Wnn37SdWSirF27VmwijtGFCxdKk4YM43379onhUxGcPn0aq1atQlRUlPqJa2dXtgdt6P7779e8RRHggIAAMUQ4h8fGxmqssjqBc0OrVq3EDOKx7z4+PmIG8/qRI0dK64zM1bCwMDFaKoKysjLk5eXh/fffx6xZswBAB1CsWrVK38U19rJly7RuYfuCg4O19qaekL+/v+yWIsgcs7169VJfulZVkKFIO0pLSxObk+Ni1KhRuj93wgRnLkG8/PLL2gRfSjg7qFKRazn4XAW2zg62/KfgiwnKXBysWrVKlHsGyby8vOTAuTjjAuhSg6enJ7Zv3w7AKXgYFxenSZuCxywJudQwZcoUTYwUQywqKtJilosebjIuRbieMMFFpGVZWpRxUZSTk6NnQdovT02Jj4+XLTNAUF1OUsnMzJSIHsuWunXrpvskDT0hIUGLN276uTnOzMxUmRbLvLhIcjeefPJJneDAYGFERIQW5iwj6Nevn4JqBOeByMhILXq5IGMApzqge/fuomzTznbt2qWSAYo0nzhxQnR1JjZos+np6Xo+XHxXF8Hco0eP6jQQbm569OghKj9P+snOzlaQim2n/Z48eVKbRm4auNF3Nxo1aqTNATfF2dnZmge5YczOzlZQ4IUXXgAAlaKFh4crUEF/yw1mdUB+fr42MQxs16pVS6eqMEi6fv16iSNz4zR69GgA9kkp3LzSHji/XmgUFBRg+/bt6N+/v+6DY8WyLPl9iqkeOHBAwQ2Wrnbv3l0+k+KoDodD/UO75VyQmZmp72IQsqrWBt7e3mjcuDEyMjLkO+jjDhw4IOFzboBr1aqFL7/8EoC9hgHsZAX9CIVS+/Xrh08//RSA89nQrxQUFGiO4TjlPFoV8PT0RJ06deBwONRfHCNNmjSRoC2Dbi1btpQ98vpZs2bJ1hhEWbdunYLFHI8MTlx99dV6nnyGP//8s4KPXBv4+/urTIpBjlatWuk7KgKWpoWGhirpQ5/Yt29fJV04J9eoUUNji/fj4eGh9zJI2KFDBwU0aLccuwcPHlTZOvt+4cKFSj4xQDlz5kyd1sWAcllZmZ5vReDn54eYmBjMmDFDZZ8cg76+vgoYMVhYu3Zt7RV4nb+/P77++msAzuBjaGio/CxPKGSb1qxZI+Fv2uZrr72m7+dhCLNnz1b5OufLTZs2VYtyZ1PWZGBgYGBgYGBgYGBgYGBgYOBGWIZdcHFhWVYqgEPuvo/ficYOhyPity4w7avWOG/7gD9+G037qjWMjcK0r5rD2ChM+6o5jI3CtK+aw9goTPuqOSpko1UBE5wxMDAwMDAwMDAwMDAwMDAwcCNMWZOBgYGBgYGBgYGBgYGBgYGBG2GCMwYGBgYGBgYGBgYGBgYGBgZuhAnOGBgYGBgYGBgYGBgYGBgYGLgRJjhjYGBgYGBgYGBgYGBgYGBg4EaY4IyBgYGBgYGBgYGBgYGBgYGBG2GCMwYGBgYGBgYGBgYGBgYGBgZuhAnOGBgYGBgYGBgYGBgYGBgYGLgRnu6+gf81WJblcPc9/BdIczgcEb91QXh4uCMqKuoi3c6FxU8//XTe9nn6Bzi8QsIq/JllfmUVvrZGTuVipR6llbocuRlHz9s+4I9vo6Z91RrGRmHadzZCQkLQoEED+8PT0gAAfn5+OHHihP4OAEFBQdi/f3/l77hyqBIbrVevHhyOM9+Sn5+vthUXF+v15OTkynz078EF78NmzZohKyvrjNeKiorg4+MDAPD39wcAZGRknHNdFeC87fP19XUEBgbCz88PBQUFAABPT3vJXFRUpN+DgoIAAKdPn0ZmZiYAICLC/uiCggKUldlrgNLSUl1/9OjRC92es1EhG/X19XUEBAQgMDAQp06dAgAEBwcDAAoLC+HhYa9JeO/+/v4oLCwEYI8/AMjNzZXdNmzYEABw6tQp+Pr66u8A9Bx8fHz0PPn5NWrU+D3P5Lxt9Pf3d4SGhp7xXbyPsrIy3Tfb5+3tjZo1awIAjh8/DgDw8vI64z4Bu1/5nOiP+L6CggKUlJQAcD7LnJwcPY/8/HwAQEBAgO6FtuTl5YWTJ0/yPedtX1BQkCMiIgKnT5/Wa+wX3gs/F7D7olatWronAPD19dV90gZycnLkdzg+OSYLCgoQFmavgS3LAmD7Jj4j3ou/v7/GNHHq1CkEBgYCAA4fPlwhGw0JCXFERkYiJydHz4mfUVBQgPT0dAAA+7moqEjPPyAgQO3m/dGPBgUF6f7Z/6mpqQCA8PBwfT/bmpKSAm9v7zOu9/Dw0Gt8Pr6+vnru6enpFerD8PBweHh4yDbYXwUFBfp83refn5/6hP7G19dXf+ffiouLkZ2dfcaz4dgtLi6W7dNeHA6H7pvPJTQ0VN/BviwsLJQN7d69+7zt8/HxcQQGBqpNAHRfYWFhZ9g+YD9nvla/fn1d7/p+tsF1LAM4wwbr1KkDwJ5P+Pmu/cbnxt9p+97e3nqGSUlJFbLRqoAJzhhUBofOd0FUVBQ2bNh0Me7lgqNGDeu87fMKCUOT2x6p8GcWXpZX4WuDl/md/yIX+GZUPPADAOs+++t52/cHwB+9jaZ9lz7+6G284O3r0aMHXnnlFQDArFmzAACxsbF67brrrgMAxMXF6fcqRJX03/3334+ioiIA0MJ5586das+xY8f0t+eee64qbsEVF7yN06ZNw6JFiwA4F9NHjx5FdHQ0AKBDhw4AgC+++AJff/31hf76s3He9gUGBmLw4MGIjY3Fjh07AEAL/qSkJG1Q+vbtCwBYvnw5Fi5cCAC46667AAB79+7VhoubnLi4ODz66KMXsi2/hgr1X0BAAK655hp0794dn376KQCgT58+AIDExERtyLjhvvzyyxX8bNeuHQBg3bp12gz/4x//AAB8+umnaNGiBQBg48aNAJyBgqioKOzevRuAc8MXGhqK8ePHX/A2hoaGYty4cXA4HNqo5+XlqU28b/ZNVFQUhgwZAgB48cUXAdhBU76Xgbj9+/frOb377rsAgOHDhwMAdu/erYBN//79AQA//vgjWrZsCQCypY4dO8o2GMyrU6cOpk6dCgBYunTpedsXERGBF198EatXr9aGOjY2FoC9weZ3MWi2Zs0ajBkzBgCwbNkyXd+7d28AwGeffQbAtuVrr70WANC0aVMA0Jjcu3cvRo0aBQAKHCQnJ6svV65cqfZddtlluhcA+Oijj3DVVVcBAO67774K2WhkZCTefvtt/PDDD3pOPXv2BGA/6w8++AAAMHjwYADA4cOH1Z8dO3YEAGzYsEEBHQa2e/furSAAgwUzZswAANxzzz3ywSNGjNDfGjVqBMC54ff39weT0fRtzZs3l31/+OGH521jeHg4JkyYAH9/f+zcuROA08/s2bMHjRs3BgAcOXIEAHDZZZfpOxcsWAAAaNmypYJ6vD41NRWLFy8+49kkJiYCsAOP9MFt2rQBYPvk7du3A4ACiYMGDcKXX34JAOjUqRMA2/ZHjx4NAOjWrVuF/Gj//v3RoEED2cH3338PALjpppsQGRkJwDkGZsyYId/KMZiQkKBnwgBLWlqaAoz0LQw0rVq1Sv7k888/B2CPY/Ybx3Pz5s1lFx999BEA2wdwTho7dqzb1momOGNgYGBgYGBQbTFq1Ci88847ACC2TKtWrbQh4mKKi/JLEcXFxVq8ciFev3597NmzBwBw6JC9Tmzbtq17bvC/xN69e8U8YMBpxYoVWLt2LQBoQd62bduLEZw5L4KDg9G7d2/MmjVL2flx48YBsDcyzJRzg+/n56fNe48ePfQZzFZz48XNaXVAYGAgevbsCcuyFDAie2Du3LkKQHBTOn36dAWeDh48CMDezDRv3hyAc+PbvHlzbQQ5Xtn3x48fV7CA1zMwdKERHh6OsWPH4pNPPsGWLVsAAHXr1gVgbzyZjV+/fj0Au1/pZ1544QUAwHvvvafAAzeBkZGR+O677wBAQQxm3hs2bHhO0CcvLw+7du0C4NxAFhYWYt++fQCAjz/+GIBtG7Vr165w+/Ly8rBlyxa0b9/+DNYLYG9AmzRpcsb1paWlmDlzJgDgxhtvBGAHJhjgIGPE29sbrVq1AgAFGq688kpdv3fv3jPaFxkZqffefvvtAGz7IfOB7KmwsLBzGBAVaeNPP/2Edu3aKbD21ltvAbBZMmPHjgUABUa7desmFgl95pVXXqlAGIMNjRo1Un8ymMagU1lZGT755BMAdiAWsMcF5xnXMc0AGP2yl5eXApcVQVFREQ4dOoQaNWooOE8WWXJyssYWmVxdu3aVP7rmmmv0GewnoqysDN26dQPgZIrUq1dP/2cQj31Zu3ZtBRCJTz75BN27dwdgB80BYMqUKRpLFUFgYCDi4uJw/Phx9d/NN98MwGaMMfC7atUqAEDr1q3RuXNnAMDrr7+u6+hPUlJSANhMNfogzie8x9atW+P+++8H4Az67NixQ8E4Bo5r166NTZtsMkFMTAwAO6BbWRutCpjgjIGBgYGBgUG1xdatW5Ut5IZj1apVWmxxYXwRGAlVhv79+2PJkiUAoAV5bm6uMou//PILACdt/VKDv7+/Nk3cFI0YMQKXX345ACdbaOvWre65wbOQm5uLTZs24eGHHxZbhJvpRx99FP/3f/8HANpQhYaG4umnnwbgDEiUlZXpd5bl/fWvf714jTgPuDFct24dhg4dCgCywYcfflgbIWaan3vuOQUO2e5GjRppHJLdtXHjRmWu3377bQAQI+X999+XLb/88ssAbPZNVSA3NxcbN26En5+fAp+HDx8GAEyYMAFPPvkkAGe/lpaWKjjCzWLt2rUxbdo0AMBjjz0GwA4YsD/Zdm5sv/rqKz0vbjKPHTumZ0k/VlBQoI0/y4qCg4PRrFkzAM6AzW/Bw8MDfn5+WL16tcpvGPjavn27WBEMSDVu3Fj+5NtvvwUADBgwQPfLIMXgwYPx0ksvAXCWazF42qlTJzFmGHRxHdubN2/W+2j7fG/NmjXPCSKcD0VFRTh8+DBycnK0kWdwYvDgwWojg17r1q3Ta9y8v/POO3rG06dPBwBs27btnO/i9a1atRKj4oorrgBgB2SWLl0KwJkMaN++veYjBjUXLFggZklFkJaWhvfffx+33XabfCED1oGBgQqEMED67bffKug1YMAAAHYQinbL4FxsbKza88033wAA+vXrB8AOpjHYQ7v09vbGyJEjATiDXwEBAWKsMBGyf/9++YiKgEFIh8OhYCzHTmJiInr16gXA6TN37typscRgd2pqqoLh9913HwA72MIgCoO7DIqnpqbi+eefBwDcfffdAOwADgPjHNubN2/GP//5TwDO4N7atWs1/7oTRhDYwMDAwMDAwMDAwMDAwMDAwI0wzBkDt6KgAChPPKA8mYbTp4F6oeVaLeX00LLAYDfcnYGBgYGBuxETEyNKNDOES5cu1e9kW0ybNg2DBg1yz03+l3j11VeV+UtISAAADBs2TBleaj9UpuyhOiExMVH0epYWJCYmSqzy1ltvBQDpHrgbZWVlyM3NxbJly5Qdp4bI448/LlYEmQK7d+/W7ywJCQ8PV+kHS4R69+6tUgF3o6SkBBkZGQgNDUVcXBwAZ4lPx44dVUrAzHvz5s2ViZ47dy4Au7/I2uDYO3TokJgzN9xwAwBnNj49PV1ZeGbghw8fjhUrVlzw9hUXFyM5ORmHDh1Sf7myVchQYH/4+/tj3rx5AJwMtb1796ot1PXw9PRUGQTHKllDgYGBytBTW6Njx45iX1H3ol69emIykLU0ffp0lURVBGTOFBYWqtSGrINly5apjIWlWtdee62YFWTotG3bViV3ZKbExMSIoUCNL47T9PR0lVCRATVixIhzSp1KSkrkqw4cOADgTNHZiiI7OxuLFy/GLbfcIhaHK1uF2in/+te/ANhjjz6T/RASEqLSLd7z0aNHxcDh2GSfL168WGOYNpKRkYHWrVsDgNgeGzZsENvoq6++AgBcffXV8nMVQUBAALp06YLrrrtOGmq87169eun7ybg6efKk5gKWP82ePVtjkJpCu3fvFouKJWccYyNGjMBPP/0EANIbmjdvnkq/yK4KCQnR93ft2lWvUX9m/vz5522fp6cnIiIicODAAdxyyy0AnM8qNDRU/UGtol69eklXhraSkZGh/qCN7tq1SwyX5cuXA3CKCu/Zs0fzCOfUyy+/XMydYcOGAQD+/ve/q6yJTKKcnBxp+bgThjljYGBgYGBgYGBgYGBgYGBg4EYY5ozBRYVHQTkjplwNPCnJAzy1rjwwjxMngGTYAmz1Iu2oeGam3kIyjRg3/iXZYtaUJ0UQGWm/DgAorx0tM7FIAwMDg0sOe/fulSAltSMeffRRZeCoI3Gp6rEAwNNPP63sLrOfp0+fRpcuXQDxDVvjAAAgAElEQVRAgqLM1F5qGD58uLQ7mOUNDw8XA2HNmjUAnHoO7kZwcDCuueYa7NixQ2wLMgDuvfdenezypz/9CYCtmcDMOvVzSkpKlC3mdRTUrU6Ii4uT+CaZMRs3blTmmkyBbdu2SdeJWeuioiJlnanlEBwcrJN6qGfCbHjv3r3x4YcfAoCOa7766qurpF3Z2dlISEhAhw4dsGHDBgBO7Y68vDyd0kOmRVpamgRtqa2TnZ2t04EmT54MwGZuUHOI4qaurAPqV5BNUr9+/TM0QwCbCcdxQHHhxo0bS+ukIsjPz8e2bdswZswYsXTIjmjVqpXacMcddwCwGYb0J3z2X375pdrHe5w3b560ZsjYINOmTZs2OoGLp8bt27dP45csjVatWkkzhOMiIyOj0mKrderUwfjx47F//349TzKOVqxYIQYIhYGTkpLkR3laU2ZmphgitL3Ro0cjPj4egJMZRqZP165dxRrjaU0zZ87UsyUbzMvLS+0h2+XgwYNiGVUEQUFBiIuLg4eHhz6D/fbdd99Jh4b2O3/+fPlIaspMmzZNwvH8WbduXTHTyDahVs3cuXNx7733AnCyGC+77DLNLdR/GTRokNhFHCN5eXmVEjUnA7FPnz5irpDllZWVJc0mspdq1aol/SKysCIiIqSNQw2eTp06aU4cOHAgAGD16tUAbEYZWUPUk8rPz9fY4/ffd999YlzRnzVv3lyaOBVhBlUVTHDG4KLBo6TIjrwAQDnFs01SkoInKFceb9YsDOW+RLVOvr7++hwGZ7hGjYoKhkeSvWgKDY12XsOTOxjFcfkMAwMDA4NLA0FBQVpkccOYnZ2txT8XkNwAXoqYMWMGnn32WQBO2vfJkydFIedJFNwYXGpYs2aNNqMUEs3JydFmkRtDHvHrbpSWliI7OxuhoaGyM5Z/bNy4UbbGUpc+ffpoQ8e2DBw4EG+++SYAZ0kUN4fVAd7e3mjQoAF8fX0lVDxp0iQA9maNmyRuQHv06HHGiUWAvZFkORc34WFhYSpXYACEG+GTJ08qGMOgKsVaLzT8/f3Rtm1bNGvWTMcDM9jw9ddf63du+AYNGqQNHu87Li5OZVgUeq1bt64Ec7lpJEpKSnSCEwNThYWF2khzg5yVlSW74nXHjh3TBrYiqFmzJoYPH459+/bp8xlke+aZZ9RH3MTGx8criMPSoM6dO2vTymPh+/TpgzfeeAOA8zQqBhcfeugh2QADI02bNtWJVgwuLVmyRCd0MWAQFxdX6bJMh8OB0tJSeHh4qKyJ4rAhISEq8WEZS2hoqIJGDKZdeeWVGn/sLy8vr3MCwkwALF68WOVhLB26+eabFXSjjRw8eFBlarThDh06VKqsybIseHh4YPXq1QqUdOjQAYAt+MvAMMfWm2++qXaxLUOHDtVR0Hwe+/btU3/y+XO8RUVFqXSH5Yw///yz7JHfn5CQoKO5abfZ2dk6BasioI/55ptv5DP5Mzg4WO3iaxs2bFBygs+0pKREwSmKh7dv315jhQE0nsz0448/yi9zvty/f7+OFGfwfPny5fjxxx8BOIOK2dnZEkZ2JwyVwMDAwMDAwMDAwMDAwMDAwMCNMMwZgyoDGS7U/wr2BVB+jN7hWPsIuE2ngWGRdvQaDz4IAPDo0gXB5XREUmj8AwOBcqqZf/lnBMbZ1EYPlAHlmY+wqHK2TPv2QEk5I6c8Y+JRUiQWjSlxMjAwMLg0EBAQIKo1Kfj//Oc/xSphVo/MhUsRWVlZ+PzzzwHYWUHAplWTbUK2AQUMLzWsWLFCDKcbb7wRgC0WSVo7j9gdNGiQygzcidLSUmRkZCAkJER0f7IN3njjDTEFWJLSpUsXZbIp6jpixAgd68vr27ZtW2VHR1cWDocDJSUl+OKLLyRYS3HcdevW4aabbgIAzJkzB4Bd9sFjlMmm8fHxOeeY6s2bN6sEhcfc07Z9fHxUckB7qCq2lKenJ+rUqYODBw/q6G8yR+Lj48WcoA0eOHBALBoKkGZmZqrkgln4w4cP671kW1B8d8aMGSr7IEtkyZIlEkumHxs+fLiYWHz2hYWFYj5UBFlZWVi4cCECAwPFcKGobOvWrfV8WWaTkpKio6FZirZr1y4xJti3OTk5ElBmCciECRMA2CUxfB4sb6pZs6b6lGjSpIlsn+yIWrVqSTi5omBZTFhYmPw7x5evr68YTmQ+1KtXD7NnzwbgPLZ+y5YtYg+xzHDx4sUSE2bpEO3cy8tLZV87duwAALRr107tJtuiTZs2YlM9WL5/6du3LxYtWlTh9hUWFuLQoUNo0qQJHn/8cQBOX7hkyRKVZpE5ee2116qtLKnbvXu37JXskVq1amleZHkQx2SdOnXEPuIR5CEhIbIX/mzfvr18MQWB+/btq2dZEZSWliIrKwuNGjXCU089BcDJdBk9erR8oesR82T1kIVVUFAgf0Omz5tvvqn+YPkWx9O7776L+++/H4DNkANsP0T/zDVE8+bNz/BjgC0iTlF0d8LsUA0MDAwMDAwMDAwMDAwMDAzcCMOcqaZgZunuu+9WXR3rPOfMmaPoe3XOFFI3hkK+P2/3BiJtxsyJ8tMyh215DrjtNvs/5SJeP67yRnxmeeS5PEK8N7QzWvzfQ2dct32ZHTH/7jsPPAU70kz2Dd59Fwda2d8VWf79/hfA2su8HciNqvhRhy9f8VWFrx0Zl1Gpe2nxr/sqdT0+q9zl58PvsdE777wTgPP4PoqM1apVS1kY1oWmpKTgkUceubA3XQmwfSNHjlT7KAh3+eWXq26VWYnMzEwJubG+ntkk12NWrynXVnr33Xfx888/X4ym/CrYvrZt254hkgjYmampU6cCgGqZly9frmwbr2cGpbS0VBmYxYsXA7CzIy+//PLFaMp/BNvYunVrZb2ZCXrwwQeVtWHdeHx8vGq8mUFlxru4uFhZLNYz5+Tk4MUXX7wILfl1sH2uehccg4WFhcoMMiMaFRWl+nNmUFlT36tXLwl6UhPE4XDIDtwBtm/9+vXKDNMeBw4cqOwmtTGCg4OVKePxmhxvCQkJyqwxi+3h4YHbOP9cYPj6+qJJkyZ47rnn5Nuor1JSUiJxWWYw69WrJz/DzHD79u1lm9RAyM7OlhgtbbRbt256H7OjfCbp6enKJF9IREZGYuzYsdi2bZsymGRQ5OXl6bhd6gYkJiYqG8/x07RpUzGBaKPJycnKQjPjzkxuRESE/C4/d/369fpc2u9ll10mtgBZAQ6HQ/75gQceOG/7eIRvRESEtBuYRa9bt67YTRS+LSwslK1Rw6N9+/a6Xx73XlpaihdeeAEApINCzYvVq1fLRqk/dPDgQemJcD5Zu3atRD75/YsXLxZ7o6L9XVBQgB07dqBv377SyZg+fToAe67m91533XUAbIYanx2z9927dxd7ge3x8fFRVp+vcawWFhaiVq1aesaAfSQy20vtiXr16kmvh6yxjIwM2RXH+W+hsLAQe/fuhaenp+yBGho7d+5UFp5jMDMzU3Mb1ygLFiyQPT7xxBMAbHscMmQIACdzisfz3njjjRqXFFJu1qyZGCjM1Ht7e4shQJHo3r17Sy+lIvD390f79u0RFBQkPRQ+561bt+oeaYPFxcWyQ77WsGFDzY0cH7t27ZKuCMcln0fXrl3lM3k8ekxMjNrC98XFxYmRxLH63XffaYxUFDyKecaMGWoPWSJz587VOpP9tn//fjEkqOHi4eEhn8Nxa1mWxh1ZE9TIqVu3ruYP/q1Ro0ZaO3BNsHfvXs3/tOnjx4+fwyL6LaSnp+ODDz7A7Nmzdb+xsbEAbIYHxxk1c3bs2KG+49hKSUlRv3NN1qhRI62vOSfQzn788Uf9jWMwJCREazgyuTp27Ci2GFlgruyriqCoqAjJycn45ZdfxP4hU2nnzp1i97jOCRwr/M6EhATNBezb3r17y165buX8unz5cjFx2JZu3brh3//+NwBojTd27Fhdx9fCwsIq1X9VBROcqaaYMmUKACfVyxXjxo1DTk4OAOdiobI4evQo/v73vwNwinpdaFCwnCc0lZT4o/Myu10fRj5m/zEw0BlQKXfk8QUFiC+xaWo//p+9eW2x+2vkTX4NAPAE7J+3lX/PUzfuxYZMmy63LtD+eWN7ILrEpthiU5L9s0cPZJ/20Nca/Hf4PTZKSis38FwQxsTEiE5NMcywsDBNAlVlo78Ftu/bb79FamoqACelMiQkRIt+ts/f31+CdVR5r1u3LgDg1ltv1fOgsOnf/vY3TJw4EYB72/f8889r8TRr1iwAdsCNgbSEhAQA9qKSmxQu/nmiQ0REBNq1awfAuUhp0KCBW/sPcLZxzpw5uleKL27cuFEbZJ60sWvXLm0OOOFzIfHqq69q88nN4mWXXVYtbPS1117TJt51IX72aSjffPONgjdctJA6/N5778leSa1PTk6uFu1799131UdcjK9atUobLm6G2rZtq4AO+5aL7JSUFAWduDBdv359lbUvICAAXbp0Qa1ateQXuEhev369NtXcrJ06dUqilk8//TQAYOnSpVo8r127FoAt7sjgEktGXOn4tE2eghQREVElbXQ4HHA4HIiLi9Nmgn4yMzNT44w22LZtWwULaauu7ef49PX11bzAgDYXyxs3blSJEeeLNm3aKKDRokULfScDrwyAZ2Zmyr4rAp6i4hrk5GazTZs2CoixhOG2227D+++/D8BZWjJhwgS1lSVCSUlJavO8efMAAKNGjQJg0+xZJkFh3jvvvFP3zQCdj4+PTqThPfXr10+2UlFQUDYpKUlB24ceekjt5zjhhqi0tFQn/7gK/nKzSiFRwJmU4eaSQeBWrVppI8ZxkZSUpMA3v/PUqVMqD6Kw7Zo1a1QSUZHgjI+PD1q0aIH169fL7/F+MjIyVD7CObmwsBA9evQAAAn+9uvXTwKsnP8PHz6spAznBK4Dtm7dqgAAS59SU1PlU1kGNWPGDJUK8b0TJ07E0KFDATiD0L8Fb29vREVFoaCgQKcpcS5v2LCh7IzrkdLSUgUgbr31VgB2MIU2zDYVFxcriMLxS0HWoKAgXc+AzZQpU/Dqq68CcK7tCgsLz0hiAHapE4NjFUVxcTGSk5NhWZZsiqU7DMoCzqBbQkKC7p1Bp61bt8q+6A+OHDkin8OgLoM12dnZuP766/V8AHu9xmdGv3TkyBHdCwMJ+fn5CoxXBPXr18eECRNQWFioNSWDE4GBgfqdPqhdu3b6fq5R9u/fr75gm5KSkjRn8nSr9957D4DtZ+hT+Blr1qxRUpG2NHHiRD0TjssHHnigUoLAp0+fxurVq9GyZUvND7yvjIwM+XH6sQULFijQxuBwz549FTzh/O/r66vnzMANA9Z5eXmaC/i348ePa86lLygoKNAagkHIQYMGuW296orzljVZlnX6fNdUFpZlRVmWNeo//M0q/znB9f9nXVPHsqy5lmUdsCzrJ8uy1lqWNfS/vKcJlmU9Wv77RMuy+v7Oz2lvWdaA/+ZeDAwMDAwMDAwMDAwMDAwM/nfgLuZMFIBRAOb+yt/+bFlWNoAAy7ImAVgOYAn/WB6smQ/gXw6HY1T5a40BDDr7gyzL8nQ4HBWvQSmHw+GoOK/wXLQH0BFAxRWhfgV33303ADszy6wQxbU6dOigCD5pXEeOHPmPVKySkhJFLF0zRxRvq+ooYR7srErnjmXASJumOnq/zZw5kPQYvvG1f7/3Xvt674Js/Di//LjJe22KLebPh/8HMwEAr420sxJ4xqYTJn37LTqXR0dblFP6gh90oKjEjr5680jtkhL4+npXQQv/N/F7bJSU6YEDBwJw2uCpU6eUXWG2xeFwiBrtjkg22zd48GBlBpmZ3LRpE5o3bw7ASXFt3Lix6OxkWzCbMX/+fGVAmKlISUmpFu27/vrr9cxJe928ebMybMxih4aGKuvIEhOWX0yfPl2U0X/84x8AbAaUO9sHONsYHR0t1gSZPVdccYWyMCyf27Fjh+6V9vjDDz8AsNkn/AxmdHft2lUt+nDs2LFiwlDM7tZbb8XKlSsBODN9y5YtU/aPmVAyZ/785z/rM5jlPnToULVo3/Dhw0XHZ2b7yJEjylDTLsvKylRCdLbQ3xVXXCHmAT9rwIABooZf6PaFhoZiyJAhePbZZ3H77bcDcGYfhwwZouwgs/fh4eG6Z2ZyPT09zzkCuFWrVmI5MKvJ7O3HH3+MsWPHAnD61pCQkCrpQw8PD3h7eyMtLU0+kMyf5cuXa0yRLbRhwwaxung08fLly2V/zPgmJCTI99DH0n47deqk50UWZnp6urKupPRv2LBBzBba+R133KFnSFHY30JRUREOHz6M48ePyxeQjXXdddfpSGyWG+zZs0frLDIiunbtes7RzCEhIWorGSHMEAcEBGgdR1v95ptvxOzg86tdu7aYOMwCl5aWYsAAOy9I1s35kJeXhy1btiA4OFjzNYV7HQ6HniH9XmZmpmyO5TwRERF6D9k0LVq0OMe/cBwuWrRI8wefQ40aNeSXadtBQUFiwU2bNg2APabJbKwILMuCp6cnmjRpIhulrwgNDRXThwy0Xbt2qU8oxF1YWChmGK9v2LChWDF8L1knp06dEpONbb/hhhs0P7IkaPjw4WL/UGw1NjZW760Ijh07hieeeAKPP/64mKu07eDgYM3dZM6dOHFC6xAySDZv3izGwT333APAZnTxNTIQyNzw8PBQX5JV079/f/Wbq9As/TfnobS0NPxKvv034XA4UFxcjHvvvfcM1hNgMwvJwiArIi0tTXME16U7d+7UuOIYHjp0qOZKMnD4TEJDQ8UO5prWldHH8p9x48bJp/O1nJwc2XVFkJmZiQULFqBhw4Yq12J5Iscn4GT85ubmalzSx8fGxsoHkl0UExOjMiyu0+hXT548KaYIWXFjx47VvEj2VZcuXTRu+HPnzp2VktPw8/NDq1atMHLkSD1f2kifPn3EwqKcQXR0tNrC/uvQoYOYM2QBTZs2TfMamTycLw4dOqRnyDGWk5Mjpt5jj9l7zhdeeEH+k4zB9957T37UnaiwILBlWXGWZS2zLOtzy7J2W5b1kQvLJcmyrCmWZW2zLGuDZVnNyl//wLKsG10+gyycyQB6Wpa1xbKsv7h+j8PheBVABICHAHzncDiW4EzEAyhyOBxvurznkMPheL38O26zLOtry7J+BLDUsqxAy7KWWpb1c/n9DXa5n6cty9prWdYqAC1dXtd9W5Z1hWVZy8sZOosty6pb/voyy7L+Xt7evZZl9bQsyxvARAAjyts2oqLP18DAwMDAwMDAwMDAwMDA4H8TlWXOdAAQAyAZwGoA3QGsKv9blsPhaGtZ1q0ApgK4/jc+5wkAjzocjnOusSzrYQCpAF4DcI1lWb4Oh8M1zRED4HwqmpcDaOdwOE5ZluUJYKjD4ci2LCscwDrLsr4uv2YkbKaLZ/lnnpFWsizLC8DrAAY7HI7U8mDLJAB3lF/i6XA4OpeXMf3N4XD0tSzrOQAdHQ7Hg+e5x9/E0qVLz/gJODMVgPNoPGboN27ceEb9pSvy8/OVvWLWKSwsTBHkqoZ/SXkd9LJNQLnAm8d8Wxgq+vRpPHSjXUH2y2470pmUFIz2cXZ2t1F5xgJPPCEh4FOZdkwxc7odcY3O/FnHbIeWfz7Cw3B6vy0cF1aeCYGnJ0oK9KvBf4nfY6OMWDNDzGxiq1atlLEii+Oyyy5TxtQdYLsiIyPPyYQ0adJEmT7qQ9xxxx3nCMay/tqyLGVumOmpWbPmRRuDvwa2LzY2FuPHjwcAPPzwwwDsrAuzU9SZKSsrU2aUmVTef7du3aR1QdHgBg0auLV9gLONWVlZGDduHAAog+las0x7rFGjhtiFDcp9Cq+pUaOGxOs+/fRTAHZWqDL15Rcarn1ItsV999lC4bm5ucrqMiP24osvirnF65lVPXbsmJgPrDmvV69etbDRQYMGKYNLcc5BgwZJTJZ9FhMTI59BDQL27c6dOzVm6WvatGkjZtSFBsVIhwwZoqwn/YKHh4fGEsfNyZMn1Q4yNbp3766MNO0sPz//DN0kwJnpvPvuu5VhZPs9PT2rpA/LyspQWFgIPz8/sVg4Znr06CHGCNkqe/bskQ4Os5lFRUXK6jLTmZmZKT/KI5YpSu2qJTB3rk28HjVqlNY3zO7v2bNHLBNmo5cuXVop8WdPT0/Url0bDRs21D3Sh6empsqWKIoaFham7CvH1po1a2Sv1D5at26ddBCot8C+WrNmDT77zFbuJ7OqZ8+eeq5kBtWoUUNZZdrFNddcg7fffrvC7QNs/xUTE4OlS5eK9UOmQEpKinw5dX4SEhJ0r48++igAu3/5LPh8ysrKlJHna9TTGDVqlDLYZJp2795dYvvUepk8ebL8Fv1YRkaGWBsVgaenJ8LCwpCbmyv9EbbF4XDo+znesrKyNN+RlVdcXKzvpE3/8MMPYtGwnRzPbdu2FbOBc/2cOXPEsCJbs379+upDjvu2bdtK5LQiiIqKwocffohJkyZJZ4Wsg7feekt+gT7k9ttvV/t5b9dcc40YDWTdBAYGqp9pc9QtS0tLw8cffwzA6c+OHj0qdhE/PyUlRX6H437y5MkYM2ZMhdsH2P1UVFQEDw8PtYfzb+PGjXUPFFPPyMiQhg+ZFDk5OXqu9AcbNmzQkcpkHnLstW7dWvMo2SfR0dFisPGZZGVlqW28j9TUVH1uRRAeHo6xY8f+f3vfHVhVlX29HoSQhISEQOgpSgxFEBASCCUgYEepo9KU3wijMhZUFNsoigOMOCoOKt8ogtgoiqAiZSQU6QgCEZAAEggJhJBKJIWQ9/1x31rvgQqJA9wX5qx/wJdnuOeeffY5Z++118bkyZM1JxR879evn9hXbN89atQo+QPaUo0aNcReo53l5uZqL6Q/pW0EBgbKJshY8fPzEyuOfvT48eNaI/w3e/TooZ+XB0FBQejZsye+/vpr7cU8X+zfv192Q7ZudHS0fAaf8cCBA2It8bO7775b9weyejwF1rmmaTNPP/20dJwoNN6zZ88zGICAxQrnmO1ERa+om5xO52EAcDgc22CVJzE486nHn6//F8/0ptPpdDocjnFOp3Pcb2nOeMLhcLwFoAssNk2s6+P/OJ3ObH4FwASHw5EAoAxAIwD1AHQF8IXT6Tzp+j2/pS7WFEBLAP9xPUZVAEc8fj7f9ecWWO/ikoEUSi5Y4MxL8tmg4jYvmUlJSXKwFxslftbi8HVRDQEALoEyAIDr4HNNfWuTu8YvFzh+Vqun994DXE4y1FUaE8rfN3w4knMtMa4YF504/XAZGk61xFbh2rSSc+syhmNwCfBbNsqLA50zA4oHDx4UzZIXj6lTp4q2aSfCw8NFm+SlbubMmRg7diwA9+G8Tp06osLyIMMDZ+PGjeX8ecD78ccfbQ0+EaWlpXjvvfcAuDfvTZs26eLAMeXl5SnQxsMPy3uysrK08ZKy2rZtW5Vz2I2GDRuKpk4qdlFRkajQPACdPHlSmzppyrTVRYsW6RLKA0WdOnUUnLMTR44c0WGSAcEBAwboYEk685IlS/TsHDvXZFJSksZCe2jRooUuYXZi9+7doh+/+aYlCH/11VdrLnkxioiIkP2Rlk/a+a233qpDJQ/hRUVFF20frF69OqKjo7Fx40atff7Ztm1b0bl5aQXcgSQeVFNSUhQQ5IH1X//6l3xlbKx15KHPzM3N1dosKSkBYAXHKf5/IVG1alUEBwdjxowZCvBxzbRv316lPRSQ/dOf/qRxcW5++eUXzRcv7LfeeqveEwM2FE6vUqWK9gSKV6anp6t8lN8bN26cRFAZjPTz81OApDyoUqUKAgICkJOTI5/NC/5VV12l98t/+7777tNllBeJzZs3qwMLfYyvr69KlrivcA02aNBAnzEgtHbtWpURsKRnwIAB6s7DEqGkpCT93vIiLy8PS5YsgdPplH2xXKtZs2a6eDIAERUVpbIYCvf//PPPCs7w+8uXL1cHLwaqPC9SfBe8VKWmpkoIl3N0yy23aD/ixdPPz09Bk/IgIyMDb775Jp599lmtN9pb8+bN9XeunxEjRqi0k3vd2rVrlYxg4CEiIkL7xNNPPw3AHdho2rSpEgD8Xb169dJeT6HZw4cP6+zAYMPWrVtlY+VBbm4u5s+fj7/85S8K0vH/r1+/vp6X/+bSpUv1c5ab79q16wyxV8AqdWKwg2ubgeAjR44oCMmgaO3atRUs5TsdNmyYSu94N7n22msrFFzzhMPhwNtvW9IGtJ+2bdsqgMjg4rXXXquAMJMN6enp8i98vmrVqml+WO7JIGB6errOr57BNPpqijsXFxdLmJufdezYEc8880y5x3Xw4EGMGjUKEydOVHCGfq9q1aoKMjNYu3jxYq0ldlHz9/dXwJfBxSVLlkhonGvbs7Se78izyyb3R8794MGDlaTju6xdu7bWbUVw7Ngxnfe5XzN45vmMmzdv1pzSbj/99FOVyTJwEhwcLB/MQCLPCNnZ2QrKMBg3b9487Stcu2VlZSopZIByw4YNOhvZiYoGZ4o9/n76rP/f+Rt/L4WrdMrhcFQBcF7BD6fLEp1O5zjP//bATgADPL7/Vxcj5nuP7/zi8fchsMqk2jmdzlMOhyMFgN/5nsMFB4CdTqcz/nd+zvdx9rswMDAwMDAwMDAwMDAwMDAwKBcuZEDhTlhaMncCWO/6LAVAOwBzYQn2VnN9fgJAEP4YEmExYR5wOp3vuD47VxgvGMAxV2DmOgDs47YawEyHwzER1nu4DcD/O+v/3QMgzOFwxDudzvWuMqcYp9N5rv7V/83YLgrCwsIUcWZG56WXXjqjneXFBEuISkp9SZJBUa879DNXsBN167iocn5+SC+wop5sxx3gUwa4Iv1wRUKTYdGbQ/yAmONW1DP5J+t3xKz8BHBFkI9FWdHaOj6mnMlukO7J6DcprsnJycr8MEMwcuRIUcLtREZGhpgHpB8PGzZMgofMqmW3ausAACAASURBVN12222KzDML0LdvXwBWNpDCbsyI3nfffWoLaicCAwOVsaHg3pVXXqnsDVt/LliwQD6DmQVmkmrVqqXx8X04nc5L5mPOhxYtWoiVwGdq1aqVmE20xx49eij7wowZM4YBAQEqC6V4ZkZGhleMsWfPnmIKMCNWXFws1gCZE3PnzpUtM2NGG9y4cSOGDBkCAPozNTXVK8ZXXFysDC5pzcHBwWJgUADS399f2WJm9yhK+fHHH4vpRTZUt27dJFJ+ocE2t2lpaWIz8d//8ccfVfZJdsDWrVuVrWdb1sOHD+v/7datGwArq0pWApkaFJQNCwtT2U92tkUeLiwsvChz+Msvv2D9+vXo3r27svGch3Xr1ok1x2xtRkaGstf8LDQ0VKVp7777LgDLRsmw4XOz1MbHx0c+mGwOPz8/lUnx/U6aNEl27SnwTdZYeVFWVoYmTZpordA3pKWlKaPNzxYsWCBRVs5pr169lFHnmB588EEx0+g/mbHv2LGjfh+fOycnRyLeK1asAGBl25l1JlNq1qxZEnQtL4KCgpCQkIC0tDSxAimG6evrK0YMmaCzZs3SuyCL9IorrtB+QIZPZGSkWBX0n5z77OxssdXoR8PDw1XCxrn+6quvxHZkOcKkSZPUfrw8qFu3LkaNGoXExETtY2TQfP3113rXzMCHhISIRcPsfc2aNcVYItskOTlZtsQzCs/W27dvF/uJbIaMjAyV1XCt7tixQywO+ult27ZVSEy2uLgYKSkpWLVqlcqaOI8dOnSQD+C+fuutt8oO+R42bdoke6TfWblypfwofx/nuF27dtovuT5vvPFGsfjIlJo3b558AO32+uuvFwOjvDh16hQyMjJQXFys98n9afPmzWKRsJxn7ty5OpvRfjt06KA1Sf/h6+srpgi/z/G0b99e+wIZGAcOHNDZlHbw5ZdfSqCW7ys3N1dnofIwTGrWrIkePXpg6tSpYuGR2VG1alXNF//N3NxczSvLkBYuXCh2H8cZGhqqtcdzKZmmW7du1b/FPYTCvYD7/Hr69GkxxL/66isAln1zTy0PKKzeoEEDnTPJpszPzxeTinZzyy23yJ+THdizZ0/tD1yfu3btwr333gvALafANblu3TqMHj1azwtYzCaevV9+2Wom07dvX+2rZL517dpV+5OdKLcgcDlQy+Fw7ADwCACK/L4LoJvD4dgOIB5uRssOAKcdDsf2swWBzwcXk6av6/cecDgcmwB8AGDs7/wvHwNo73A4kgDcDeAn1+/ZCmAOgO0AFgPY/Bv/VgmAgQD+4RrDNgCdzvOIKwC0MILABgYGBgYGBgYGBgYGBgYG5cF5eQROpzPQ9edKACs9Pj87pT3Z6XSeESBxOp0ZADp6fDTW9fkpWF2X/hCcTucRWGK+v/WzmQBmevz3cViBod/67t9hCfye/flwj79vA5DwG9/pfta/EeX6ezaA2LO/bycefPBBRU4ZGWYG+FLCx4O54udRWOYqHz3jg0CP/wcAThZVQQC/6Iokr7GC0xg+HECplcWJ+cnSN/kycDC6u5JkdQus+smTgQ0Nc8ZmMAvMVtr8771790osmBk0b4heA1amhVlSZip27dolLQhmUz777DPVwzOzxGzAkCFDlFVmdrEiwn8XE1WrVlVmkFmoPXv2KKvEDG5RUZGYTxQyZHY0KytL74iMG9ZzewOysrI0J6x7jouL0/MzY7R//369C2ZoKDJXrVo1ZSeZ2WKLV7tRpUoV1eEz47tjxw5l0pmJ8vPzk5YQGU7M/A4YMEDjo6YJRQ7txjfffKOMJ7PMnTt3FuuCmb709HSt0Q0bNgBwC1+2a9dOug/8GVv1Xgzk5+dj6dKlyM/Pl+A2/93jx48r00wGSGRkpIQmiZSUFOmIcM3VrVtXWW9PvREA6N+/P55//nkAbkZHRTLxFYGfnx+aN28Oh8Oh7C79h7+/v3QA6OciIiI0h2R3lZWViT1CzaSIiAi9E7K8+DtuuOEGrF9vEbOZIa5Tp46yv/Q5t9xyi/5On7x8+fIK2bOPjw/CwsKwZ88eMTjoL2JjY8XE45w5HA75Sv7bffr00bPPnj0bgDUf9KPUGqHY5uuvv679hOKj48ePl24bfXJeXp6YfTzbPf3002rvXJEx1q1bF/v27dN4PFvsUqya2eX4+HgxROgz9+3bJxujltOQIUP0rGQzkXmxatUq6QVxL2zVqpV0LmhLAwcOFBvgoYce0rNxrZcHBQUFWLduHZo2bSrtH+pYbNy4US3daT9RUVHy7WRVhoaGyofwTNK6dWv9P7fffjsAt48tLCzUM5IZ891330lAmO/D89/lO23YsKH2p/nz5+N8KCsrQ0FBAUaMGCGWBplUH374ofwbx3To0CHtb/ST9evXx/vvvw/AvdfVr19f5xsymsnUKi4uVgtj2m9sbKzYTVy7AwcOFGuB62HNmjUYNWrUecfliSpVqsDf3x+bNm0S45H6IO+8844YINRAKiwsFBuDjKEvvvhC64XvNzQ09AwWBuA+uyQlJYmFQdtOT08X64lnnYCAAPkU+oi0tLQKsbsCAwPRrVs3+Pr6yn/yvd1+++1aD2yxvn37dkycOBGAWxvuyJEjsi+yL/Pz82W3ZLuxgiImJkYaQdyHwsPD5av8PC5oXMfUspkyZYqerzzw8/NDTEwM8vPzzxAWBywmD/Vn6OtTUlJky5yftWvXij3Iz3JycuR7PdcPYPm1J554AgC0Hx46dEjj57qIjIyU36GPy8jIEGPHTjh+LenyB36JpePS3hWkMDgHHA7Hf//Cywkqxq9YsUIbLw3wu++++yO/covT6Wx/ri+0b9/euWnT9+f6SoVQ4KEL7JtrOX0KA8NVMpK4LVTBHv55bVS2hIZLQiyq5vkCM1WrOs47vuCAhs6OMfeW+/lzJpWW+7u9Gu4p93cBYP1jv92d6/ewcvnT5x0fcPnbqBnfHwdp6DxgcmM7evSoShu4sbZp00aHuArAdhtlcJAXLR5cBw4cqMM5D30JCQm/2yXvHLjgczh+/HiJivKwFRcXp0s8D1gNGzbUAZOXD9pou3btdDgkzX/Xrl1/pOzngo8vPj5eh3qKHAcFBenQyQNyXl4ehg4dCsAtOstExIEDB3QZ4u/auHEjPvjgg4o8CnCRbHThwoUSVOXhfvXq1Tq8sqyradOmmmsGNjwvTgzS8UJWrVo1PPfccxV5FOAizOGoUaN0sPYU4qYdsqQiPT1dpS38GUuUUlNT9RnLatq1a4d77rmnIo8CXITxPfTQQ1o3DLBkZWVJWJSXm927d+vCy2AhL1RXXHGFgk8MvBUWFl6U8QEVH+POnTsVoOIlPyYmRmUhvHy1aNFCwV+W+NB+t27dquAF382CBQuwbNmyijwKcBHm8KOPPtIln88YHR0tv8ExV61aVfsdL+W84KekpIB9TFh+uHLlSsyYMaMijwJchPG98MILusCyTMXhcCgAwY6GKSkpCswxsMI12bhxYyXVWJpZUlKi8h4Girdt26azAH/X4cOHlYTr06dPuWy0devWzqVLl2LChAkKyLJ0c/v27fJ9tMuIiAgFF7gH1KhRQ3sEvx8YGCjhZpa8MaCdlZWlUj4m2KpXr46NGzcCcJerl5aWKmDE5I9nOeBTTz113jGGhYU5+/bti549eyq4ysBncHCw9nCWG37++edKULD73MqVK+Vb+dmcOXPkh9iRkIGsLVu2qESMJYYnTpyQwDBt+sSJEzo7cK/Nzs6WfU+dOvW842vSpIlzwoQJSE1Nle2x7Ktt27YK0NLHr1+//lfJoaioKNkrA9mFhYVag9zrGKg+dOiQxkKBc8Dtl/n9WrVqqZyJ6/Ojjz4SiWH8+PHlstGLgQvCI3A6nVEX4vcYGBgYGBgYGBgYGBgYGBgY/K/BFHlcxmDksFq1amplx6xMZYNvQTbAUixXeUhJoBXd7VH0DVBgMVSeXGNRTK99zsetJlx+8orBJcblZKO/hcttfMy2EMzmfPDBB8oSTp8+HYD3lKVVFMySMgtDZkndunVFp/bMBHoDsrOzVSrClqJvvPGGqOnMaPv7+4vqzCwps1SfffaZMn5kMXgLTp8+Lbo+s1+BgYHKopGy7uvr+6tnZ1a0evXqynhybpnt9gZMnz5dZWW0qw4dOoj1Qkp2zZo1NdfMsBLbt29XFpyMIY7ZbkREREgMlL5i7dq1ynKTNVRYWCjKPf0LRUkXLVqk9ch38M477AthL8rKymSbZAzs2rVLWX6WJdSrV0/lLmTHsKXyI488osw+9wmWQHkDVqxYISYFy2KqV68u5gyZB/7+/hJSZZklGW9FRUVig7HVMplgdmPu3LlitrJk8uqrrxaLkq2mo6Oj9cwUZ/W0Y4ofc5y0VbsRFBSkVvQUoS4pKRGjgeVjb7zxhsRkWd5DBtTixYvFziSD1N/fXwzTMWPGALCYwfSzZA2Hh4fLDsqL9PR0vPDCC+jWrZv2NjLUwsPDNQ9kWcTFxYktwRLexo0bq8yS+0hxcbFsk0LeLGdp3769npOC3sHBwSph43P4+Pjo79yLAgICtN+WB0VFRdi3b59K/Tzh4+MjBg9LiYYMGSJhX5YRHjt2TGXzLK87evSoWCksJ6WvHD16tMZOXzVp0iSdb8hoi4uLU2khmW+ZmZkVOsMWFhbixx9/hNPpFPOM7asTEhLw+uuvA3CfQ6ZPny6WIddWdHS09jUyf/39/bUvcJwsqWvZsqWYTJy/lJSUM8r7AMtm6J859kWLFuncZCcupCCwgYGBgYGBgYGBgYGBgYGBgUEFYZgzlyEYwWcWrqSkRKJIbCVWWSCx4JXb1Eo7cYMV6e0RZUXPUVSE1XX6AwBeGWplKuDXDCWlJvborbicbPS3cLmOj0J7zIpS4PSVV17BG2+8AcAt7Mbsd2UDszXMELPm2rOGnuO/5pprNG47Ub9+fWmN8Bk9GSQjRowAYAlwU0uB2SNq5vTp00c1+Mwes87bbnTv3l06JdSy2L9/vwQpmR1r3bq1MqjMkrF+PCYmRhpJ06ZNA4AKi1NeTEyZMkXZZ4rYLlq0SBls6jdt2LBBwo3UCGKWv7S0VNlDtpvlf9sNp9OpzDMzrzk5OWKhkU1y5ZVXqm0shSOpZzFs2DDptJCF07t37woL4V4MxMfHizXId9+yZUv5E47pxIkTykJTz4JZ86NHj4q5R4YQv+MN8PX1lVAxmZHff/+9WDTMxh86dEisCa5bzltsbKyYDZMnTwYAPPHEExVqz3uxEBgYqGz8I488AsDSpSLLkL7ngw8+wGuvvQbAEmIG3LoekZGRYmKQBUUxYrvhdDrl/6k3unv3bjEx7rrL6rMye/ZsacxwHyRDpLS0VGuWrJVOnTpJz2TPHks7sbi4WH75zTffBGDtK2wGwdbI50NISAj69OmDTz75RGwf/rls2TLtd9RxKiws1PqiHsy3334rNhP3vYSEBIlDL1iwAICb4ZOamiq2yWeffQbA2oNo39SyadOmjVhvEyZMAGCde8gMLA9CQ0Nx5513IjMzU4xJzsesWbPELiPj9aeffhJLhmfJsWPHivlBzaRu3bpJ15DnFTKeZsyYIVZR+/aWpMq4cePE9Ob+s2/fPjGnOF89evSQzXO/Ohd++eUXbNmyBb169ZJWDdk4b7/9tnw79+Thw4eLycXnT0lJkb/h+TIrK0taXXw33POjoqK0P/A80L9/f/ni/v2t++KXX34p9iLn9OabbxbjzU6Y26uBgYGBgYGBgYGBgYGBgYGBjTDMmcsQVENna7UlS5ZUOp2LKqUWKya7wKpDDO3SRe2WXA1jgM+saDeiosDGbyXNrIyFj48xbm/G5WCj58LlOj52a2LNNzM3jzzyiLQ9OOYmTZrgn//856V/yP8S1NVhxposktzcXDEvWGvPzjF2w+l0qkaeGbGSkhJlgP7xj38AsPQAOAZqmLATTO3atZXxY3bcW7RnWrZsqcwWmSDZ2dnSJGEmd+fOneogwiwn22yeOHFC3R2ob0Lmlzdgx44dyvjSvvbu3as1xvH/61//kvYFmTNsDbpz505plPz73/8GANx7b/m7C15M1KpVS9lrdusYN26cMrLMBiclJYlFQ1YX56l169Z6D+xoxcy13ahRo4banlOjpHv37sqGs+1rnTp1ZJOcK+pgtGnTRuuX32eXFG9AlSpV1KKWXU6qV68un8LMdcOGDZVxpy4GGRuHDx/G/fffD8Ct/USdDLtRWFgoBiLtbOHChcquk7F2xx134K233gLg3ifYwfDAgQNimZDVESgKuL14++23MXjwYABuDZn77rtP+lT0i7t37xZDgXs9GRYfffSR9E/I7jp9+rQYKfSxXbt21TqeNGkSACAxMVHdEMuL/Px8LFu2DB07dhQLkkzB+Ph42df//d//AbDmkCwadlcaM2aMuvKxffrnn3+ueSJLiL/jwQcflFYXdXh++OEH+SN2ips2bZrOemTY+Pj4YObMmeUeX25uLhYsWIA+ffrggQceAAC1Je/UqZM0VtiZLykpSWuJ/vSnn37SHHIv2Lp1q/wLNVToZxo0aKC9nlqIjz76qBg7ZHo5HA599vDDDwOw2HMVaRVeu3ZtDBkyBN999506StFGsrKydC7mZ40aNRIThp+Fh4erGxf1ajZs2KAx0C64r5w+fVpnAu6lR48elc3ze4MGDVJbctrykSNH5JfshLm/XkYgJe1vf/sbAPfB7aWXXrLtmf4ISksBX9dhLJSH54EDcbK+RZ0NmGldKuDa/LOj4xAHi64GV+vtspBQdtI+bwttg0uHy8VGfw+X+/hYPkDBO27Yx48f12WY9FNvoXJXFKTSDhs2DIBbJLBWrVq/EqAlbdpuREVFiY7PsogaNWpI7I40+xo1auiiy9aVvHjk5uaKpkxhYB5a7UZeXp4o8jykRUdHq6Uo27f269dPpSW8KJPSHRkZqVasPNzyd3kDZs2apffN4FhoaKguTDx8P/7445rXVatWAXBfGoODgzXus79jNw4ePCi/QXHHLVu26BLIQ3pWVpZKCkhD79evn3524403AgD+8pe/AIBXlBUC1iWW756Xx40bN0okmOMsKyuT/+BYWJKQmJiogCODVnffffclGsH5ERwcrKAE7SsgIECln7w4NW/eXBdjBpsYNE1OTlZAg++GZSp2Y9CgQSrjYUlnbGysfCYvqrNnz9Z+xwA212nt2rXllxiY8xZB4L59++qdswxp0qRJKjdkScwNN9yAl19+GYC7nJJjGD58OBITEwG4S0aqVasmm2YZZlBQEB599FEAUEBm8ODBZ7Q2Lg9OnTqFtLQ0FBcXq6yM9rJz5075d5ZA3nPPPQrEcH9+9913FYhhwqhPnz56VgZYxo4dC8DysSy94RmuSZMm+vv8+fMBWGcdluowObVixQq9T54dzoWAgAC0bdsW+fn58tUsc0xPT1cgl+Nr3ry5/Auff/Xq1dqzOU9XXHGF7JAJDa7TgoIClZr16dMHgDVfPN/RP991110K3lAkeOXKlSonKg/S0tLw7LPP4u6771ZgjHv0NddcozXCJMSsWbMUEKTPyMzMVJCaQevMzEwFic4u5W7WrJkCdCyn9Pf31/rlu3r33XcV9OL39+7dq3/fTpiyJgMDAwMDAwMDAwMDAwMDAwMbYTgFlwlCQ0MVCWWG5ptvvgHgXdTtc6EKrKiu79HDgCsyLPpLbi4Cts0/4/vrfCwaaaeZrwHDhwMAsmG11w4sNYwZb8PlYKPnwv/C+NjukLR0Zo1OnjwpVgkZHLGxsWJxVBaEhoYq00IqMEUd69Spo6wjGRcVaZl5MTFs2DCMGzcOgFuMeurUqWIVMNO2b98+0bWZRaKoblBQkLL2FHNmS0u7ERISokwmM9sNGjSQQDVFE9PT0yXkySwhBSr9/f1FdWa2bMCAAZg9e/YlGsW50aZNG42HWdjatWsri7pkyRIAlrglM6ycH1Lfhw4diqVLlwJwM4Zoz3ajc+fOov7zmf75z3+Kuk72U1xcnLK0zHazZCIiIkKsIpbx3XTTTcqU24nGjRurnIF2dvz4cflDCrA+9NBD8h9kZ3CufHx8VBp0dnmNN6C0tFRlnmyb3blzZ71/Cqvm5uZqnTKbze8MHDhQwqScS5a/2I3Tp0+LETNgwAAAFiuBoqVkioSFhek9MKPPvcDhcCiTT6YF/Y3duO2222RPZLZGRkZixowZANwMtaNHj6r8iXs4RVoDAgLkM1955RUA1n5INgL/LCwsFEOCwrtr165VKU55cerUKWRmZuKqq67S3LD8MycnR89FoeFPPvlEc0Ob6969u56LZ7E2bdrI/thimqyTLVu2yCZZYhMSEqI9hQzpDz/8UGxTzvXhw4dl3+VBWFgYRo0ahdTUVD0PhfkHDRok5grtMTg4WOXKf/7znwFY/mbXrl0A3CVXfn5+YpnwM/7+QYMGiU1DNtKqVav03rjXzJ49W+eFTz/9FIDFdiGzsTxo1KgRXn75ZRQXF2svpr9zOp0qlSfzCnCXR3Kcs2bNUrkWz5mpqak6E/B5uA++//77ap1O1nBeXp4+8xSTJ5uW7MWHH35YjCey/+yAYc4YGBgYGBgYGBgYGBgYGBgY2AjDLajkYD3i0qVLlYHav38/AOC5556z7bkqApJjfF3WmB0YgZDRjwEAXMkX+PgAVw60BMngylB0yrUi+mWjH0OVw1ZmLbSxVaNYZuKOXoPLwUbPhf+l8VF8k1lrZotatWqltpr8fvfu3cXK8HZ4jpHZOWa/mWXx9/dX1oaaCaGhoZf6UX8TI0eOVLaW7KaxY8dKAI9ZwICAAGXFKIBHIUvPuWKW31uYQVu2bFHmk3PVoEEDZdg4H6Wlpfoe2SXUHcjNzVWWjroDFc3iXkwEBwcrm+3JQuMYqR8QEhIiP8P5YfY4NzdXwp3MBvfs2VNaEnbiyJEj0mRh1vall17SHDC7fuDAAY3rmWeeAeD2o8uXL1dGn+1eN23adIlGcG5Mnz5dLbTJetm4caMYapzHlJQUCXOSfUIG1KZNm+R3+D7IQPEGzJo1S7oyZOitXr1aekhsOf3jjz+KhcCMNHU4tm/fLhHyF1988Yyf2Y3S0lKMHz8egFtEPTw8XBl/6mLs3btXeki0Q7Iqd+zYIUYp2Zfesk8sXLhQz0S/mJSUpGcnS2Pw4MEaH5lPbDOdlJSE0aNHA3Brk2RnZ0t4lSy3WrVqaR3QBrZt24ZmzZpV6Jn9/f3RqlUrxMbG4vTp0wDczM7Q0FD5+wcffBCAxZQkc4Vsmnr16kn4lczKjz/+WAxnstvIspg1a5ZYUhQzXrx4sQSD6afj4+O17/LMFxkZqbNQeZCamopHHnkEISEh0peilt/8+fPlK+kH/Pz8xDLhunnllVe0Jzz//PMALP2q119/HYBbf41z//PPP4sxQxbjyy+/LKZTx44dAVj7Cltck3HVuXPnCvnc7OxszJ49GzfddJP8BG1vz549YmaR5TNixAjZC7VfOnfurLlZvHgxAOs9U0SeDCnawKxZs8QWIgt12bJleoe0y+TkZJ1hyb6ZNm2a5rkiws4XGiY4U8lBkUTS/ADgsceswEZlKSnwhbUAS0pdnZl+Wge4DphFRdZn17T3Bajy7qLon+xiUU0DUAa4FiKDMgz4ABe4vKm0FFWO5ZT769NbLCj3d1883LtCj+KTuKVC379YmDt3rmyNtM9Vq1ZJNIx09a+++kp2WplsdPDgwRL7o/DmDTfcIKfOzW3NmjWVcnxjxozR5u3ZTYUlFhznsWPH1AGGInKk5Xs77rvvPm3WVPaPjo7WfF55pSU2PmXKFAnlUoCPh4aCggIdWBm84OHUbgQFBUlMlgeg5ORk7Q+88J0+fVqf8bDFsQ8aNEiXEHaoIKXbbiQnJ+uQxcvhxo0bdYHlYTUsLEwdNShMTdpyYmKiDuv8/1gy5A2oW7euDsVce8HBwRJM5OWoc+fOmk/aI/3u+++/r4vL2YFEuxEUFKT3z/kqKCjQZY3dfxISEhRYo0gw/VJUVJTERXlYZ5DAbvj7+4uGz4P+iBEjJKzKy1NgYKAuNyxL4KWpQYMGmlNe6L0pgNizZ09d1tnRJC0tTT6FvtXPz08XG84T5zknJ0eldywX8paOVPn5+RgzZgwAt/84efKkLoH0mR9++KH2TK5PluDFx8frsk//yYuw3ejbt69KQdixJi4uTiUxI0eOBGCtRZbeTZ48GQAk5Ltr1y4FYniJrlGjhoJZ50NFzwyZmZmYOnUqpk6dWq7v9+/fX+uJz1lQUKCuddyzu3TpoiAwA6dceyNHjtTP2Jmqffv2Kvu9kAgNDcWdd96J5cuXq4yGZXCDBw+W6C+fe82aNQrmMhjRpEkTBcVYhjRmzBitUQrgDx06FIBll+wIxUDW4sWLJULOP38LFU24BQQEoF27dtixY4fKwRhQ27Nnj8ZKgfewsDCVKbMM67PPPtP833XXXRozy+v4fQa7+/XrpyQF7TY4OFg+lWWznucFBiijo6Plx+2EoRcYGBgYGBgYGBgYGBgYGBgY2AjDnKmkYJSP2SYAeOKJJwBUnmw2AFTJzcZJPyuaGVBqZegxfDjgivBe46Khjzt1CuNc2Qu4IsNwMWdQWooyH98zfq8RA750yMnJEZ2bAri+vr7K/jJKfu2111ZKG+3UqZMyuMyO+fj4KONAdkxAQEClHF92drYyeyz3aNeunbKZzMrPnj1bLTQr0/gAoGXLlsp6sozHz89PNFdmSUNDQ1U2w+w+s6b79u0TK4VZU9qF3SgrK1MLcAoYp6SkiFXAEqaCggJlvsjyIhvq/vvvF8uCzCCWAdmNq666Sv5kyxaLMRgTE6PsL+fP399fWWtmPMl6a9OmjSjRZGZ4C/MJsPwo1yHtKioqSiVLnJurr75a2Uayul577TUAFotx7ty5ANyMN74vuzFz5kxlo5m5dDgcKrNj29RDhw5pzDzf5jvjhQAAD9pJREFUcA737Nmjsw8zox9++OElGsG5QQYa4J6XLVu26DlZ6rBmzRr89a9/BeC2wy+//BKAJSTPFscsjeFa9AaUlpaqVJL+cdGiRSoTYIvhBx54QEwFrluyEg4dOiRRVrL9yHCwG7NnzxZzksyRdu3aoXnz5gCgMpHhw4ejRo0aANyipWToHTlyROcE7jUUKrUbBw4c0Noik2vTpk24+eabAbjH/Msvv0gcmKUutOPi4mIxgsh2CAwM1Dq2G4MGDZIf5X7dqVMnlSe3bt0agFUaxc9YTsRW0hERESpHpDgsyykvNHJycvDFF1+gWbNm2m/pDwIDA2VXU6ZMAQD87W9/k7A6BXwzMjI0Ls9242TosYEDS7aLiorEGuU4Kfh7oeF0OlFcXIy4uDikp6cDcJfjVq1aVawtlnLdeuutEj/mWIYMGaKSOzLVw8LCzphLwC1oXVhYqFInns/79+8v1h+Fy4uKinDTTTcBAF599VUAlt/l2rAThjljYGBgYGBgYGBgYGBgYGBgYCMMv6CSgsJrzCIBUC12pYKfH1Qy/r0V5Ubv3ih5yhK1Sq9uZY+eB9yMGVc0nJ3XGjf2BVwaM4Yxc+nxww8/KGvCDOeAAQOUKaSQ5YkTJyqljUZHRyuzxzr6hQsXqh0gmRUpKSmVcnxNmzZVpqB///4ALG0SZjSYfRk1atQ5a5G9GY0aNZLOAevkO3TooJptsk2eeeYZiXWSdUKB3ZtvvlkZ8a5duwKwt9WiJzwZP8uWLdPnzDx5ase89dZbANzCjcxor1+/Xtl6ihtSS8Ju+Pj4yJ/QLq+88koxMCjKuXz5cmWtmRGj/kBBQYEyh8wCr169+tIMoBzIzMxUJpCZ9tatW2vuONZ///vf8j1kLFIw98SJE8oOkhXUtm1brxAEjo+PF1PCUyz27LasjRo1EjuDYptk8UVERPxKCJjZb7uxdetW6VqRUVBUVKTno+ZKQUGBxEapy0YGWN26dTFv3jwAVoYccGsmeAPS0tKkG0Hf169fP7HvuK5OnDihOaTmHNkJTZo00Z5J0Lbtxv333y//8b1L2/D48eNiYJDd1bx5c515OnfuDABilc6cORN///vfAbiFY8eOHYuXXnrpEo3i93Hs2DHtEzyrtG7dWkwNapM0aNBAemy0W7KdatasqT2S+6C36D4Blo22atUKgFvgeNGiRfI51FWrXbu27JVnVDKIXn31VY1t7NixANy6NBca1apVQ926ddGlSxcxQMnyrVmzpnScyOg6evSo2HQ8j/Tq1UsMSTKjkpOTxcRhu2+Ob9GiRRITJluI+8aFRllZGYqKihAfH69nJPO6e/fuEmWmZlFCQoKYyWSLHjp0SGwb6svs2LFDPuj6668H4F6DzZs3/5VQt9PplD4Ux+rn56e9ke8mOztbZ0U7Ya6ylRBdunSRYGNlR5lfAKrkWpcDkNraty9cLDw8uMVyQle0K4Kz+9sAgPRp1kbC7cAXJSjBmWVNBpcOkZGREp/kQaxhw4Y6jD377LMA3IKWlQ0HDx4UhZsHm3vuuUeXYB7cuClWNpSVlWlTI1W7SpUqiIyMBOCm13uLOOwfwdy5cyW+yQv6nDlzfhV0S09P13g5n7TbxMRE3HjjjQDcQnWk2NqNGjVq6ADGQ86XX36Jli1bAjhT3JIHDwYASL1fvXq1xvXUU08BAE6dOnWJRnBupKWl6TDJ8pFFixaJxsxDeJ06dTRmllPwkrF//36VWLLkx1uo+IBVwsNLKwNLEREROlByz9+8ebOEDUm597xU8RBLu/UWG42IiJAfpa3WqVNHgReWjnz33XcquaP98cKcn5+vDh68/D/++OM6sNsJz9JCXq5ycnLkRzg+h8MhPzJu3DgAbvHmDRs2yH45vttuu00XKLvRvn17lRDwYnTjjTdqr6AAaVJSkjrrsPyXdulwOHRBpigpA1F2Y/PmzXr/DLQFBAQoGMGgm+feQX/K/XHy5MnyM7TLN9988xKN4NxISEjAk08+CcDtTxwOh8ooGaSZOHGigvfcO9jx7+jRoxg0aBAAd1DRm4IzR48eVUKF4vHFxcUqYWbA7Pbbb1ewmONnCXdCQoKCMRSiZeLxQsPf3x+tW7fGzJkz5bOZLJszZ44C7+xwRtFmwH1GOXnypPY0NqPIzs6Wv2VZMwNsJ0+elE9loPhiiXI7HA74+PhgzZo1EjdmwOTQoUMq0eXZMy8vT80YGIS64oorFIChdEJaWppKzT766CMAkG0nJyfLXvkuHQ6HzrlMXKxdu1blXZ4dtpiwshOmrMnAwMDAwMDAwMDAwMDAwMDARhjmTCVE165dJcBG7N+/X9mayoTSUsDXFd0tibai2Gh8Jfq6WK7XFli0c2fsGGzqYtGYQ1zDdDFMAR8flBbprwaXGC1bthRNkRlPzww9s/ArV66slDb6ww8/KDNIobLt27erbZ+nmGxlHF9SUpKym6WuHvSff/656LykP4eFhVXK8QFWmRYzvizVadasmeizzLSdPHlSGbZhw4YBsN4FYGVezhYEvuuuuySCaSeSk5PxzDPPAHBngBo1aiS6NrNqzZs312fMbFNcsFevXrJzsk3OLj+wC127dsXXX38NwL3eysrK1FKa7XonTJiAV155BYC7HShtevLkyRLLpQ30799f2V+7ERcXJ3ZBz549AQCTJk1S6QvLJ9atW6fMNRk2zBK+8847ajXKzD9/p91ITExUBpelTA6HQ2Vnx44dA2DNNUUwyeTid4qKiuSDWArDtWs3rr76ar1zZnybNm2Kb7/9Vj8HrLX49tsWC5hjoBhmQECAfgcFOmNiYi7RCM6PNWvWyDeQyXbHHXdojPQzpaWlmk+Oh/OUlZWlNcm1ylJFu1GrVi3td1xTgwcPlq/k3lCjRg2tUbLvyBoODw8Xg4jvg2wvu/Hoo4+KtUXG5BNPPKFzG8d0/PhxCcezyQHFXNu3b681SLaFt/hQwBLR5X5AJtDx48flQ+644w4AFluNZzj6UbJq8/Ly1GKZtsq98ULj1KlTSEtLQ9WqVcW4ImOwf//+KrthG+/vv/9ejB+yX/Py8vD4448DcLM+Ro0ahfHjxwNwr0EyhK666iq1sWfpM9frhYaPjw9q1aqFffv2yZfR7+3bt09lWzyXREdHS0yczNDdu3frzkvb82wIQPYLS9ZjY2PFkOJek5OTI+Z7hw4dAFiCxFyrP0ku46hKN+1kLBrmjIGBgYGBgYGBgYGBgYGBgYGNMDyDSg62C+3Ro4cikJUNJ6OsKPA2S38NHTsCt3dx1fzNtD58uOMmvFlksWjy61sR/5o+J12/wXvqXf8XMW/ePNVkM6udmpqqWk/aZUFBgWrNKxPq1asn9gTrfjdv3iwhM4qL/fDDD5VyfD4+PsqecP66dOmibCbreps0aYIJEybY85D/JYYOHao6ZmZEv/rqKwmwMoNWp04daXyQeUFBxJiYGLEQmCmmPoHdaNKkieyQ2Z6mTZv+qsZ60KBByg4yU8SM4u7du/V3zjm1XOxGcHAw+vTpA8D97sPDw5UR5Bw9+eSTZ2hbAFBGcdq0aarRZ7YsNjYWzz///CUaxbmxc+dOZQKjo6MBAL1791bm9LbbbgNgienymfkZs6Bt27aVlgAzviNHjsTatWsv0Sh+H/Xq1ZPQNFkX8+bNkygnWU3Dhw9Xy2LqtFDc8f3330evXr0AuLXM2MrYbjRq1EjsLjKbVqxYIbFxZnDz8/Mxbdo0AFbLaQDyqw0aNNBa5ZjJDvMGBAYGSueJbel3794t4VH6jzlz5siHUNSTazU4OFhssHfeeQeAW5fGbpSVlWkvpGbeiBEj1ICDLJkBAwZg9OjRANzzRH2T+vXra+8kW+i6665TJt9O9OjRQ7om1FLJzMwUM4jrMzw8XAK01AUi06awsFC/gywqb2g9TOzcuVMaXPwzIyNDbBMKiZeWlv5Kl4v2GxwcLBbRf/7zHwDuu9aFxqlTp5CZmYmff/5ZtkdmZ2ZmprR/uH7Kysp0liETcc6cOdKtIgPmySefVJts7vncE1u1aqW9htps1J650KhatSpCQkKwf/9+PRvvC926dRNLkvpM/v7+Yvhw/r799luxiegPGzZsKCYM9zrum4mJiWKDceyebeT5HI0bN1arbZ4J7rzzTul92QkTnKmEmDhxIiZOnGj3Y1wwuNYVOkVZtEkcBbL9rEUUOnQoAGCcD3DSz9oc/FxWW+YToN9hypnsQ9euXUW55MV3+fLloh9SUZ6bS2XDwYMHddCkCGDHjh3lwHmB8OySU5lw/fXXi05LwbYWLVpos+bmxQ2wMqJhw4a47rrrALgvFa1atcKqVasAuIUAr7nmGvTu3RuA+zI1ffp0ANahlF1y+DNv6dYUFhamQyTX4O7duxUs5KHlscceQ1paGgBgzJgxAID33nsPAPDnP/9Zh9UXX3wRgPd0a9qyZYsuEPQ1O3bsUPCJQbZDhw4pYEZqO8srk5OTUa9ePQDuUjVeqLwBvXv31vtmWcyuXbvURYLzGhgYKLo655WH9XvuuQczZswA4BYoZQme3WjXrp3WD/1MdHS0AsIst3j11Vclxsn3wa5aaWlpEo9lkMBb9pUjR46cQaEHrKAwu/4wQJaamqpx8ULF8fr6+uqCTJ8Uovpt+5GRkSHRY9plSkqKbJNrrWfPnrrcs+MWgxP333+/bJMleLww2424uLgz5gkA7r33Xl30eNnNysrSfsLAPn2tr6+vbJJ7KIPHdiMmJkbdw2hXAwYMUMkkxdanTZumizGFWNlFrFu3brrQ09fyjOcN6Nevn0qYWYYeHBysZ2bA7IsvvtB8MtC2YMECAJbvpB9lAPymm25SqeGFRFBQEBISEnDzzTdjypQpANzBym3btkkolyVB0dHRSgxy/5s4caKCLfQtWVlZ2hfYlbJjx44ArJLm5ORkAO6y5r7shnuBUVxcrFJrBlMYnM/Pz5eIL89ZtWrV0rx4dgrlu+da2rt3rwI7XG9cu7Vr11ZJLG00Ly9PySZ+1q9fP+2hPL/v2rXLKwTYTVmTgYGBgYGBgYGBgYGBgYGBgY1wMENjcGngcDgyAdjPmfpjiHQ6nWHn+oIZn1fjvOMDLv8xmvF5NYyNwozPy2FsFGZ8Xg5jozDj83IYG4UZn5ejXDZ6MWCCMwYGBgYGBgYGBgYGBgYGBgY2wpQ1GRgYGBgYGBgYGBgYGBgYGNgIE5wxMDAwMDAwMDAwMDAwMDAwsBEmOGNgYGBgYGBgYGBgYGBgYGBgI0xwxsDAwMDAwMDAwMDAwMDAwMBGmOCMgYGBgYGBgYGBgYGBgYGBgY0wwRkDAwMDAwMDAwMDAwMDAwMDG2GCMwYGBgYGBgYGBgYGBgYGBgY2wgRnDAwMDAwMDAwMDAwMDAwMDGyECc4YGBgYGBgYGBgYGBgYGBgY2Ij/D94kWwezNBYdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ff1fc8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot the perturbation step by step.\n",
    "plt.figure()\n",
    "grid, row_labels = [], []\n",
    "col_labels = [\"Input\", \"Analysis\", \"Ordering\"]+[\"Step {}\".format(i+1) for i in range(steps)]\n",
    "\n",
    "for perturbation_analysis, method in zip(perturbation_analyses, selected_methods):\n",
    "    row_labels.append([method[-1]])\n",
    "    samples = list()\n",
    "    \n",
    "    # Reset the perturbation_analysis\n",
    "    perturbation_analysis.perturbation.num_perturbed_regions = 1\n",
    "\n",
    "    sample = test_sample\n",
    "    analysis = perturbation_analysis.analyzer.analyze(sample)\n",
    "\n",
    "    # Divide into regions and order them according to their analysis score\n",
    "    # TODO backend channel ordering\n",
    "    aggregated_regions = perturbation_analysis.perturbation.reduce_function(np.moveaxis(analysis, 3, 1), axis=1, keepdims=True)\n",
    "    aggregated_regions = perturbation_analysis.perturbation.aggregate_regions(aggregated_regions)\n",
    "    ranks = perturbation_analysis.perturbation.compute_region_ordering(aggregated_regions)\n",
    "\n",
    "    # Perturbate for some steps\n",
    "    for i in range(steps+1):\n",
    "        # Plot the original image and analysis without any perturbation\n",
    "        if i > 0:\n",
    "            perturbation_analysis.perturbation.num_perturbed_regions += perturbation_analysis.regions_per_step\n",
    "            # Perturbate\n",
    "            sample = perturbation_analysis.compute_on_batch(sample, analysis)\n",
    "        # Apply common postprocessing, e.g., re-ordering the channels for plotting.\n",
    "        sample_to_show = mnistutils.postprocess(sample.repeat(3, axis=-1))\n",
    "        analysis = mnistutils.postprocess(analysis)\n",
    "        # Apply analysis postprocessing, e.g., creating a heatmap.\n",
    "        sample_to_show = np.clip(input_postprocessing(sample_to_show), 0, 1)\n",
    "\n",
    "        samples.append(sample_to_show[0])    \n",
    "    # Plot analysis\n",
    "    analysis = method[2](analysis)\n",
    "    \n",
    "    # Add analysis and ranking\n",
    "    samples.insert(1, analysis[0])\n",
    "    samples.insert(2, -ranks[0][0])\n",
    "        \n",
    "    grid.append(samples)\n",
    "  \n",
    "eutils.plot_image_grid(grid, row_labels, list(), col_labels)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
